Name: Data Analyst: Data Analytics + Visualisation with Python, Power BI, Tableau, SQL & VLOOKUP Course
Price: 41 GBP
Availability: InStock

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			<h2>Overview
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					<p><span>This comprehensive course on </span><b><span>Create Smart Maps in Python and Leaflet Level 3</span></b> will deepen your understanding on this topic.</p><p><span><span>After successful completion of this course you can acquire the required skills in this sector. This </span><b>Create Smart Maps in Python and Leaflet Level 3</b> comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.</span></p><p><span><span>So enrol in this course today to fast track your career ladder.</span></span></p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.<br /></p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p><span>There is no experience or previous qualifications required for enrolment on this </span><b><span>Create Smart Maps in Python and Leaflet Level 3</span></b>. It is available to all students, of all academic backgrounds.</p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p><span>Our </span><b><span>Create Smart Maps in Python and Leaflet Level 3</span></b> is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</p><p><span><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></span></p>					</div>
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			<h2>Career Path</h2>		</div>
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					<p><span>Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business &amp; Management, Admin, Accountancy &amp; Finance, Secretarial &amp; PA, Teaching &amp; Mentoring etc.</span></p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>6 sections • 26 lectures • 03:41:00 total length</p></div><div><div><div><div><div><div><div><p>•Introduction: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Installing PostgreSQL and PostGIS Part1: 00:10:00</p></div><div><div><p>•Installing PostgreSQL and PostGIS Part2: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Installing Python Django in a Virtual Environment: 00:10:00</p></div><div><div><p>•Installing and Configuring Atom IDE Part1: 00:10:00</p></div><div><div><p>•Installing and Configuring Atom IDE Part2: 00:03:00</p></div><div><div><p>•Creating a GeoDjango Application Skeleton: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Adding a Spatial Database to our Django Backend: 00:09:00</p></div><div><div><p>•Updating our django models file: 00:08:00</p></div><div><div><p>•Registering our model in the admin file Part1: 00:09:00</p></div><div><div><p>•Registering our model in the admin file Part2: 00:10:00</p></div><div><div><p>•Registering our model in the admin file Part3: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Updating the settings file: 00:07:00</p></div><div><div><p>•Creating the layout page Part 1: 00:09:00</p></div><div><div><p>•Creating the layout page Part 2: 00:10:00</p></div><div><div><p>•Creating the layout page Part 3: 00:07:00</p></div><div><div><p>•Creating the index page Part 1: 00:10:00</p></div><div><div><p>•Creating the index page Part 2: 00:07:00</p></div><div><div><p>•Updating the index page: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Creating datasets: 00:10:00</p></div><div><div><p>•Displaying data on the map Part 1: 00:10:00</p></div><div><div><p>•Displaying data on the map Part 2: 00:02:00</p></div><div><div><p>•Creating a legend: 00:10:00</p></div><div><div><p>•Creating the barchart legend: 00:06:00</p></div><div><div><p>•Creating the barchart Part 1: 00:10:00</p></div><div><div><p>•Creating the barchart Part 2: 00:09:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

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Create Smart Maps in Python and Leaflet Level 3

General Admission

<div>
	<h3>Description:</h3>
<p>This diploma in C++ and Python programming course is a great way to get started in programming. It covers the study of the C++  and Python group of languages used to build most of the world's object oriented systems.</p>
<p>The course is for interested students with a good level of computer literacy who wish to acquire programming skills. It is also ideal for those who wish to move to a developer role or areas such as software engineering. This is a great course to develop your coding skills. It teaches key features of imperative programming using C and is an ideal preliminary to the Object-Oriented Programming using Python. Join the course now!</p>
<h3><strong>Entry Requirement</strong></h3>
<ul>
<li>This course is available to all learners, of all academic backgrounds.</li>
<li>Learners should be aged 16 or over to undertake the qualification.</li>
<li>Good understanding of English language, numeracy and ICT are required to attend this course.</li>
</ul>
<h3><strong>Assessment: </strong></h3>
<ul>
<li>At the end of the course, you will be required to sit an online multiple-choice test. Your test will be assessed automatically and immediately so that you will instantly know whether you have been successful.</li>
<li>Before sitting for your final exam you will have the opportunity to test your proficiency with a mock exam.</li>
</ul>
<h3><strong>Certification:</strong></h3>
<p>After completing and passing the course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates can be obtained either in hard copy at a cost of £39 or in PDF format at a cost of £24.</p>
<h3><strong>Why choose us?</strong></h3>
<ul>
<li>Affordable, engaging &amp; high-quality e-learning study materials;</li>
<li>Tutorial videos/materials from the industry leading experts;</li>
<li>Study in a user-friendly, advanced online learning platform;</li>
<li>Efficient exam systems for the assessment and instant result;</li>
<li>The UK &amp; internationally recognized accredited qualification;</li>
<li>Access to course content on mobile, tablet or desktop from anywhere anytime;</li>
<li>The benefit of career advancement opportunities;</li>
<li>24/7 student support via email.</li>
</ul>
<h3><strong>Career Path</strong></h3>
<p>After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market.</p>
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					<td colspan="4">Python 3 Beginners</td>
				</tr>
								<tr>
					<td colspan="4">Module 01</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction</td>
					<td><span>FREE</span> </td>
					<td><span><i></i> 00:29:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Starter Examples</td>
					<td> </td>
					<td><span><i></i> 00:33:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Learning C Concepts</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 02</td>
				</tr>
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					<td><i></i></td>
					<td>Data Types and Inference</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>Sizeof and IEEE 754</td>
					<td> </td>
					<td><span><i></i> 00:33:00</span></td>
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					<td><i></i></td>
					<td>Constants L and R Values</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
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					<td><i></i></td>
					<td>Operators and Precedence</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
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					<td><i></i></td>
					<td>Literals</td>
					<td> </td>
					<td><span><i></i> 00:26:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 03</td>
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					<td><i></i></td>
					<td>Classes and Structs</td>
					<td><span>FREE</span> </td>
					<td><span><i></i> 00:22:00</span></td>
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					<td><i></i></td>
					<td>Enums</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
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					<td><i></i></td>
					<td>Unions</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
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					<td><i></i></td>
					<td>Introduction to Pointers</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
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					<td>Pointers and Array Indexing</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
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					<td>Using Const with Pointers</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
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					<td>Pointers to String Literals</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
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					<td>References</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
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					<td>Smart Pointers</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
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					<td>Arrays</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
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					<td>Standard Library Strings</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
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					<td>More Standard Library Strings</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
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					<td>Functions</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
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					<td><i></i></td>
					<td>More Functions</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
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					<td>Function Pointers</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
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					<td>Control Statements</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
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					<td colspan="4">Python 3 Intermediate</td>
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					<td colspan="4">Module 04</td>
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					<td><i></i></td>
					<td>Installing Python</td>
					<td><span>FREE</span> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>Documentation</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
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					<td><i></i></td>
					<td>Command Line</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
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					<td><i></i></td>
					<td>Variables</td>
					<td> </td>
					<td><span><i></i> 00:29:00</span></td>
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					<td><i></i></td>
					<td>Simple Python Syntax</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>Keywords</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Import Module</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
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					<td><i></i></td>
					<td>Additional Topics</td>
					<td> </td>
					<td><span><i></i> 00:23:00</span></td>
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								<tr>
					<td colspan="4">Module 05</td>
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					<td><i></i></td>
					<td>If Elif Else</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
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					<td><i></i></td>
					<td>Iterable</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
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					<td><i></i></td>
					<td>For</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Loops</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Execute</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Exceptions</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Types</td>
					<td> </td>
					<td><span><i></i> 00:24:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 06</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Number Types</td>
					<td> </td>
					<td><span><i></i> 00:28:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>More Number Types</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Strings</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>More Strings</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Files</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lists</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Dictionaries</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Tuples</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Sets</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 07</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Comprehensions</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Definitions</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Functions</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Default Arguments</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Doc Strings</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Variadic Functions</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Factorial</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Function Objects</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 08</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Lambda</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Generators</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Closures</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Classes</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Object Initialization</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Class Static Members</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Classic Inheritance</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Data Hiding</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Python 3 Advanced</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Iterators and Generators</td>
					<td><span>FREE</span> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Regular Expressions</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introspection and Lambda Functions</td>
					<td> </td>
					<td><span><i></i> 00:27:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Metaclasses and Decorators</td>
					<td> </td>
					<td><span><i></i> 00:24:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Modules and Packages</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Working with APIs</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Metaprogramming Primer</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
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					<td><i></i></td>
					<td>Decorators and Monkey Patching</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
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					<td><i></i></td>
					<td>XML and JSON Structure</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
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					<td><i></i></td>
					<td>Generating XML and JSON</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
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								<tr>
					<td><i></i></td>
					<td>Parsing XML and JSON</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
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					<td><i></i></td>
					<td>Implementing Algorithms</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
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					<td colspan="4">Certificate and Transcript</td>
				</tr>
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					<td><i></i></td>
					<td>Order Your Certificates and Transcripts</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table>

John Academy

Programming Fundamentals

Computer Programming

Professional Skills Course (PSC)

The Complete Python 3 Course: Beginner to Advanced

<strong>The course is crafted to help you understand not only the role and impact of recommender systems in real-world applications but also provide hands-on experience in developing complete recommender systems engines for your customized dataset using projects. This learning-by-doing course will help you master the concepts and methodology of Python.</strong>
        <p>Have you ever thought how YouTube adjusts your feed as per your favorite content? 

Ever wondered! Why is your Netflix recommending your favorite TV shows?

Have you ever wanted to build a customized recommender system for yourself?

Then this is the course you are looking for.

We will begin with the theoretical concepts and fundamental knowledge of recommender systems. You will gain an understanding of the essential taxonomies that form the foundation of these systems. You will be learning how to use the power of Python to evaluate your recommender systems datasets based on user ratings, user choices, music genres, categories of movies, and their year of release. A practical approach will be adopted to build content-based filtering and collaborative filtering techniques for recommender systems.

Moving ahead, you will learn all the basic and necessary concepts for the applied recommender systems models along with the machine learning models. Moreover, various projects have been included in this course to develop a very useful experience for you.

By the end of this course, you will be able to relate the concepts and theories for recommender systems in various domains, implement machine learning models for building real-world recommendation systems, and evaluate the machine learning models.

All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/Recommender-Systems-with-Machine-Learning</p>
        <h2>What You Will Learn</h2><p>Explore AI-integrated recommender systems basics<br>Look at the basic taxonomy of recommender systems<br>Study the impact of overfitting, underfitting, bias, and variance<br>Build content-based recommender systems with ML and Python<br>Build item-based recommender systems using ML techniques and Python<br>Learn to model KNN-based recommender engine for applications</p>
        <h2>Audience</h2><p>No prior knowledge of recommender systems, machine learning, data analysis, or mathematics is needed. Only the working knowledge of basics of Python is required. You will start from the basics and gradually build your knowledge in the subject.<br><br>This course is designed for both beginners with some programming experience and even those who know nothing about data analysis, ML, and RNNs.<br><br>The course is suitable for individuals who want to advance their skills in ML, master the relation of data analysis with ML, build customized recommender systems for their applications, and implement ML algorithms for recommender systems.</p>
        <h2>Approach</h2><p>This is a comprehensive, easy-to-understand, self-explanatory, to-the-point, and practical course with live coding and two in-depth projects covering complete course contents.<br><br>Every module has engaging content; a completely practical approach is used along with brief theoretical concepts. At the end of every module, there will be a quiz, followed by its solution in the next video.</p>
        <h2>Key Features</h2><p>Build recommender systems using ML from the perspective of content-based and collaborative filtering * Implementation of ML with data analysis on real-world datasets of movies and Spotify songs * Learn to program with Python and how to use ML concepts to develop recommender systems</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Recommender-Systems-with-Machine-Learning</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. AI Sciences Introduction</p><p style='margin: 0'>This video provides an introduction to AI Sciences.</p></td></tr><tr><td style='padding: 15px;'><p>2. Instructor Introduction</p><p style='margin: 0'>This video provides an introduction to the instructor in detail. You will look at the course's structure and the things you will learn by the end of this course.</p></td></tr><tr><td style='padding: 15px;'><p>3. Overview of Recommender Systems</p><p style='margin: 0'>This video provides an overview of the recommender systems and their importance.</p></td></tr><tr><td style='padding: 15px;'><p>4. Fundamentals of Recommender Systems</p><p style='margin: 0'>This video helps you out with the fundamentals of recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Overview</p><p style='margin: 0'>This video discusses the projects you will be working on in the later sections of this course.</p></td></tr></tbody></table><br><h4>2. Motivation for Recommender System</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Recommender Systems Overview</p><p style='margin: 0'>This video provides an overview of recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Recommender Systems</p><p style='margin: 0'>In this video, you will get to know more about recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>3. Recommender Systems Process and Goals</p><p style='margin: 0'>This video helps you understand the process and goals of recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>4. Generations of Recommender Systems</p><p style='margin: 0'>This video helps you with generations of recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>5. Nexus of AI and Recommender Systems</p><p style='margin: 0'>This video talks about the nexus of AI and recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>6. Applications and Real-World Challenges</p><p style='margin: 0'>This video helps you understand the applications and real-world challenges, and how recommender systems resolve these issues.</p></td></tr><tr><td style='padding: 15px;'><p>7. Quiz</p><p style='margin: 0'>This is a quiz video on the concepts learned in this module/section.</p></td></tr><tr><td style='padding: 15px;'><p>8. Quiz Solution</p><p style='margin: 0'>This is a solution video of the quiz for this module/section.</p></td></tr></tbody></table><br><h4>3. Basic of Recommender Systems</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Overview</p><p style='margin: 0'>This video provides an overview of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Taxonomy of Recommender Systems</p><p style='margin: 0'>In this video, you will study the taxonomy of recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>3. ICM</p><p style='margin: 0'>This video talks about the ICM (Item Content Matrix) that is used to store the data of recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>4. User Rating Matrix</p><p style='margin: 0'>This video talks about URM (User Rating Matrix), which is used to store the data of recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>5. Quality of Recommender Systems</p><p style='margin: 0'>This video explains the quality of recommender systems in detail.</p></td></tr><tr><td style='padding: 15px;'><p>6. Online Evaluation Techniques</p><p style='margin: 0'>This video talks about the online evaluation techniques of recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>7. Offline Evaluation Techniques</p><p style='margin: 0'>This video talks about offline evaluation techniques of recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>8. Data Partitioning</p><p style='margin: 0'>This video explains data partitioning.</p></td></tr><tr><td style='padding: 15px;'><p>9. Important Parameters</p><p style='margin: 0'>This video talks about the important parameters of recommender systems.</p></td></tr><tr><td style='padding: 15px;'><p>10. Error Metric Computation</p><p style='margin: 0'>This video demonstrates error metric computation.</p></td></tr><tr><td style='padding: 15px;'><p>11. Content-Based Filtering</p><p style='margin: 0'>This video helps you with content-based filtering.</p></td></tr><tr><td style='padding: 15px;'><p>12. Collaborative Filtering and User-Based Collaborative Filtering</p><p style='margin: 0'>This video helps you with collaborative filtering and user-based collaborative filtering.</p></td></tr><tr><td style='padding: 15px;'><p>13. Item Model and Memory-Based Collaborative Filtering</p><p style='margin: 0'>This video helps you with the item model and memory-based collaborative filtering.</p></td></tr><tr><td style='padding: 15px;'><p>14. Quiz</p><p style='margin: 0'>This is a quiz video on the concepts learned in this module/section.</p></td></tr><tr><td style='padding: 15px;'><p>15. Quiz Solution</p><p style='margin: 0'>This is a solution video of the quiz for this module/section.</p></td></tr></tbody></table><br><h4>4. Machine Learning for Recommender System</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Overview</p><p style='margin: 0'>This video provides an overview of the section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Benefits of Machine Learning</p><p style='margin: 0'>This video helps you with the benefits of machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>3. Guidelines for ML</p><p style='margin: 0'>This video demonstrates the guidelines for machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Design Approaches for ML</p><p style='margin: 0'>This video talks about the design approaches for machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>5. Content-Based Filtering</p><p style='margin: 0'>This video helps you with content-based filtering.</p></td></tr><tr><td style='padding: 15px;'><p>6. Data Preparation for Content-Based Filtering</p><p style='margin: 0'>This video explains data preparation for content-based filtering with machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Manipulation for Content-Based Filtering</p><p style='margin: 0'>This video explains data manipulation for content-based filtering with machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>8. Exploring Genres in Content-Based Filtering</p><p style='margin: 0'>This video helps you explore the genres in content-based filtering.</p></td></tr><tr><td style='padding: 15px;'><p>9. tf-idf (Term Frequency-Inverse Document Frequency) Matrix</p><p style='margin: 0'>This video is about the tf-idf (Term Frequency-Inverse Document Frequency) matrix.</p></td></tr><tr><td style='padding: 15px;'><p>10. Recommendation Engine</p><p style='margin: 0'>This video talks about the recommendation engine in detail.</p></td></tr><tr><td style='padding: 15px;'><p>11. Making Recommendations</p><p style='margin: 0'>This video helps you with making recommendations.</p></td></tr><tr><td style='padding: 15px;'><p>12. Item-Based Collaborative Filtering</p><p style='margin: 0'>This video demonstrates item-based collaborative filtering.</p></td></tr><tr><td style='padding: 15px;'><p>13. Item-Based Filtering Data Preparation</p><p style='margin: 0'>This video explains and demonstrates item-based filtering data preparation.</p></td></tr><tr><td style='padding: 15px;'><p>14. Age Distribution for Users</p><p style='margin: 0'>This video helps you find the age distribution for users from the dataset.</p></td></tr><tr><td style='padding: 15px;'><p>15. Collaborative Filtering Using KNN</p><p style='margin: 0'>This video helps you with collaborative filtering using KNN.</p></td></tr><tr><td style='padding: 15px;'><p>16. Geographic Filtering</p><p style='margin: 0'>This video helps you with geographic filtering.</p></td></tr><tr><td style='padding: 15px;'><p>17. KNN Implementation</p><p style='margin: 0'>This video explains how to implement KNN.</p></td></tr><tr><td style='padding: 15px;'><p>18. Making Recommendations with Collaborative Filtering</p><p style='margin: 0'>This video helps you make recommendations with collaborative filtering.</p></td></tr><tr><td style='padding: 15px;'><p>19. User-Based Collaborative Filtering</p><p style='margin: 0'>This video helps you with user-based collaborative filtering.</p></td></tr><tr><td style='padding: 15px;'><p>20. Quiz</p><p style='margin: 0'>This is a quiz video on the concepts learned in this module/section.</p></td></tr><tr><td style='padding: 15px;'><p>21. Quiz Solution</p><p style='margin: 0'>This is a solution video of the quiz for this module/section.</p></td></tr></tbody></table><br><h4>5. Project 1: Song Recommendation System Using Content-Based Filtering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Project Introduction</p><p style='margin: 0'>This video gives you an overview of the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Dataset Usage</p><p style='margin: 0'>This video explains how to use the dataset to your advantage.</p></td></tr><tr><td style='padding: 15px;'><p>3. Missing Values</p><p style='margin: 0'>This video explains how to churn out the missing values from the dataset.</p></td></tr><tr><td style='padding: 15px;'><p>4. Exploring Genres</p><p style='margin: 0'>This video helps you with exploring genres from the dataset.</p></td></tr><tr><td style='padding: 15px;'><p>5. Occurrence Count</p><p style='margin: 0'>This video demonstrates the occurrence count of the specific condition.</p></td></tr><tr><td style='padding: 15px;'><p>6. tf-idf (Term Frequency-Inverse Document Frequency) Implementation</p><p style='margin: 0'>This video demonstrates the implementation of tf-idf (Term Frequency-Inverse Document Frequency).</p></td></tr><tr><td style='padding: 15px;'><p>7. Similarity Index</p><p style='margin: 0'>This video talks about the similarity index in detail.</p></td></tr><tr><td style='padding: 15px;'><p>8. FuzzyWuzzy Implementation</p><p style='margin: 0'>This video helps you with the implementation of the FuzzyWuzzy algorithm.</p></td></tr><tr><td style='padding: 15px;'><p>9. Find the Closest Title</p><p style='margin: 0'>This video explains how to find the closest title from the dataset as per the input.</p></td></tr><tr><td style='padding: 15px;'><p>10. Making Recommendations</p><p style='margin: 0'>This video explains how to make song recommendations.</p></td></tr></tbody></table><br><h4>6. Project 2: Movie Recommendation System Using Collaborative Filtering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Project Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Dataset Discussion</p><p style='margin: 0'>This video involves a deep understanding of the dataset to be explored for this module.</p></td></tr><tr><td style='padding: 15px;'><p>3. Rating Plot</p><p style='margin: 0'>This video helps you with the plot rating from the dataset.</p></td></tr><tr><td style='padding: 15px;'><p>4. Count</p><p style='margin: 0'>This video explains the count function.</p></td></tr><tr><td style='padding: 15px;'><p>5. Logarithm of Count</p><p style='margin: 0'>This video helps you explain the logarithm of count.</p></td></tr><tr><td style='padding: 15px;'><p>6. Active Users and Popular Movies</p><p style='margin: 0'>This video explains the active users and popular movies available.</p></td></tr><tr><td style='padding: 15px;'><p>7. Create Collaborative Filter</p><p style='margin: 0'>This video demonstrates creating a collaborative filter.</p></td></tr><tr><td style='padding: 15px;'><p>8. KNN Implementation</p><p style='margin: 0'>This video explains the implementation of KNN.</p></td></tr><tr><td style='padding: 15px;'><p>9. Making Recommendations</p><p style='margin: 0'>This video explains how to make movie recommendations.</p></td></tr><tr><td style='padding: 15px;'><p>10. Course Conclusion</p><p style='margin: 0'>This video provides the conclusion for the course. We hope that you will be able to apply the learnings from this course into your professional life.</p></td></tr></tbody></table>

Packt

Data Analysis

Python

Pandas

The course is crafted to help you understand not only the role and impact of recommender systems in real-world applications but also provide hands-on experience in developing complete recommender systems engines for your customized dataset using projects. This learning-by-doing course will help you master the concepts and methodology of Python.

Recommender Systems with Machine Learning

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			<h2>Overview
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					<p>Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career.</p><p>This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more.</p><p>After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today!</p>					</div>
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					<p><span>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.</span></p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p>This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science.</p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p>The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush.</p>					</div>
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					<p>This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as</p><ul><li>Data Analyst </li><li>Data Scientist </li><li>Data Manager </li><li>Business Analyst</li></ul><p>And much more!</p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>90 sections • 90 lectures • 10:19:00 total length</p></div><div><div><div><div><div><div><div><p>•Course Overview &amp; Table of Contents: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•System and Environment preparation - Part 1: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•System and Environment preparation - Part 2: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of python - Assignment 1: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of python - Assignment 2: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of python - Functions: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of python - Data Structures: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of NumPy - NumPy Array: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of NumPy - NumPy Data: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of NumPy - NumPy Arithmetic: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of Matplotlib: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of Pandas - Part 1: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Learn Basics of Pandas - Part 2: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Understanding the CSV data file: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Load and Read CSV data file using Python Standard Library: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Load and Read CSV data file using NumPy: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Load and Read CSV data file using Pandas: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Summary - Peek, Dimensions and Data Types: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Summary - Class Distribution and Data Summary: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Summary - Explaining Correlation: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Visualization - Using Histograms: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Visualization - Using Density Plots: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Dataset Visualization - Box and Whisker Plots: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Multivariate Dataset Visualization - Correlation Plots: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Multivariate Dataset Visualization - Scatter Plots: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation (Pre-Processing) - Introduction: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Re-scaling Data - Part 1: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Re-scaling Data - Part 2: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Standardizing Data - Part 1: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Standardizing Data - Part 2: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Normalizing Data: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Data Preparation - Binarizing Data: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Introduction: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Recursive Feature Elimination: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Principal Component Analysis (PCA): 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Feature Selection - Feature Importance: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - Introduction: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - Train and Test Set: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Introduction: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Log Loss: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Classification Report: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - Mean Square Error: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Algorithm Evaluation Metrics - R Squared: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - Logistic Regression: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - Naive Bayes: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - CART: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Classification Algorithm Spot Check - Support Vector Machines: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Linear Regression: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Ridge Regression: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - CART: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Pipelines : Data Preparation and Data Modelling: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Pipelines : Feature Selection and Data Modelling: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Ensembles - Voting: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Ensembles - Bagging: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Ensembles - Boosting: 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Parameter Tuning using Grid Search: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Performance Improvement: Parameter Tuning using Random Search: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Export, Save and Load Machine Learning Models : Pickle: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Export, Save and Load Machine Learning Models : Joblib: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Finalizing a Model - Introduction and Steps: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Resources - Data Science &amp; Machine Learning with Python: 00:00:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Computing Skills

Data Engineering

Big Data Analytics

Data Science

Data Skills

Data Science with Python

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			<h2>Overview
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					<p><span lang="EN-GB">This comprehensive course on <b>Learn to Use Python for Spatial Analysis in ArcGIS</b> will deepen your understanding on this topic.</span></p><p><span lang="EN-GB">After successful completion of this course you can acquire the required skills in this sector. This <b>Learn to Use Python for Spatial Analysis in ArcGIS</b> comes with accredited certification, which will enhance your CV and make you worthy in the job market.</span></p><p><span lang="EN-GB">So enrol in this course today to fast track your career ladder.</span></p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.</p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p><span lang="EN-GB">There is no experience or previous qualifications required for enrolment on this <b>Learn to Use Python for Spatial Analysis in ArcGIS</b>. It is available to all students, of all academic backgrounds.</span></p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p><span lang="EN-GB">Our <b>Learn to Use Python for Spatial Analysis in ArcGIS</b> is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</span></p><p><span lang="EN-GB">There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></p>					</div>
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					<p><span lang="EN-GB">Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business &amp; Management, Admin, Accountancy &amp; Finance, Secretarial &amp; PA, Teaching &amp; Mentoring etc.</span></p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>1 sections • 12 lectures • 02:15:00 total length</p></div><div><div><div><div><div><div><div><p>•Module 01: Introduction of batch processing: 00:15:00</p></div><div><div><p>•Module 02: Link a series of processes to one: 00:12:00</p></div><div><div><p>•Module 03: Animation of feature data 1: 00:11:00</p></div><div><div><p>•Module 04: Animation of feature data 2: 00:13:00</p></div><div><div><p>•Module 05: Animation of raster data 1: 00:13:00</p></div><div><div><p>•Module 06: Animation of raster data 2: 00:10:00</p></div><div><div><p>•Module 07: Collect data and Batch processing: 00:10:00</p></div><div><div><p>•Module 08: Hydrological processing with python script: 00:11:00</p></div><div><div><p>•Module 09: Map Algebra and Math 1: 00:10:00</p></div><div><div><p>•Module 10: Map Algebra and Math 2: 00:10:00</p></div><div><div><p>•Module 11: Display XY, Select and Export: 00:10:00</p></div><div><div><p>•Module 12: Surface and Interpolation: 00:10:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Learn to Use Python for Spatial Analysis in ArcGIS

<div id="course-pills-1">Duration<p>2.75 Days</p><p>16.5 CPD hours</p>This course is intended for<p>Complete beginners who have never programmed before to experienced developers coming from another programming language.</p>Overview<p>You will learn how to leverage the power of Python to solve tasks. You will build games and programs that use Python libraries. You will be able to use Python for your own work problems or personal projects. You will create a portfolio of Python based projects you can share. Learn to use Python professionally, learning both Python 2 and Python 3! Create games with Python, like Tic Tac Toe and Blackjack! Learn advanced Python features, like the collections module and how to work with timestamps! Learn to use Object Oriented Programming with classes! Understand complex topics, like decorators. Understand how to use both the Jupyter Notebook and create .py files Get an understanding of how to create GUIs in the Jupyter Notebook system! Build a complete understanding of Python from the ground up!</p><p></p><p>Our Introduction to Python course is designed to take complete beginners or experienced developers up to speed on Python?s capabilities, setting up students for success in using Python for their specific field of expertise.</p> <p> Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3. Learn how to use Python for real-world tasks, such as working with PDF Files, sending emails, reading Excel files, scraping websites for information, working with image files, and much more! This course will teach you Python in a practical manner and provides a full coding screencast and a corresponding code notebook to review the concepts and exercises conducted in class.</p>  <p><b>Please note, this course is able to be offered in either 3 full day sessions or 5 partial day sessions.  See the schedule below.</b></p><p>This course includes 6-months access to the full course content in on-demand format to support post-class reference and review.</p><p></p></div><div id="course-pills-2"><ul><li>Command Line Basics</li> <li>Python System Setup</li> <li>Jupyter Notebooks</li> <li>Python Data Types</li> <li>Key Data Structures</li> <li>Logic and Control Flow</li> <li>Functions</li> <li>Debugging</li> <li>Modules</li> <li>Object Oriented Programming</li> <li>File I/O</li> <li>Testing</li> <li>Decorators</li> <li>Generators</li> <li>Automation of Tasks</li> <li>Web Scraping</li> <li>Graphical User Interfaces</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Introduction to Python training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Introduction to Python course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

Introduction to Python

<strong>This video course gives you an insight into applied data science concepts using Python. With the help of interesting activities and hands-on coding exercises, you'll learn about data science, extended data analysis, linear and logistic regression, data visualization, k-means clustering, and decision trees.</strong>
        <p>There is an ever-increasing demand for storing different types of data in organizations. Be it a small or big organization, employers want to keep records of their employees, customers, accounts, feedback, and so on. However, maintaining such an enormous collection of data is not possible without the help of data science. This video course will help you to learn the important concepts and theories of applied data science that you need to know to store, manipulate, and visualize huge chunks of data.

The course starts with an introduction to applied data science and a tutorial on how to set up a Jupyter notebook. You'll then go on to understand linear regression using Boston data. As you advance, you'll discover data visualization techniques and explore time series and data evaluation. You'll also get to grips with extended data analysis with the help of a temperature analysis activity. Toward the end, you'll be introduced to k-means clustering and gain a solid understanding of decision trees.

By the end of this course, you'll be well-versed with applied data science concepts and be able to apply your skills in the real world.

All the codes and supporting files for this course will be available at- https://github.com/PacktPublishing/Projects-in-Data-Science</p>
        <h2>What You Will Learn</h2><p>Gain a solid understanding of applied data science<br>Get to grips with extended data analysis using an example data set<br>Find out how to perform linear and logistic regression<br>Become familiar with scattered and advanced scattered plot<br>Develop skills to plot time-series data<br>Explore k-means clustering and define centroids for clustering</p>
        <h2>Audience</h2><p>If you are a data science enthusiast who wants to develop your skills to effectively maintain your organization's data, then this video course is for you.</p>
        <h2>Approach</h2><p>Using detailed explanations, coding exercises, and activities, this video course helps you to get up and running with applied data science concepts.</p>
        <h2>Key Features</h2><p>Understand applied data science concepts with the help of engaging activities * Get to grips with data visualization, plotting, and clustering techniques * Discover how to make the best use of decision trees</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Projects-in-Data-Science</p>
        <h2>About the Author</h2><h3>Eduonix Learning Solutions</h3><p>Eduonix learning Solutions is a premier training and skill development organization which was started with a vision to bring world class training content, pedagogy and best learning practices to everyone's doorsteps . Eduonix aims to identify and provide the best learning and training environment. It identifies industry veterans and content creators around the globe and bring it to the global audience using number of intuitive platforms for easy and affordable access to quality content. Eduonix offers easy to understand online courses and workshops for everyday people. If you have ever wanted to learn a new skill, but don't want to attend four years of college to do it, we have a solution for you.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Extended Data Analysis</h4><p></p><br><h4>3. Regression</h4><p></p><br><h4>4. Data Visualization</h4><p></p><br><h4>5. Activity</h4><p></p><br><h4>6. Time Series</h4><p></p><br><h4>7. K-Means Clustering</h4><p></p><br><h4>8. Decision Tree</h4><p></p>

Data Visualization

This video course gives you an insight into applied data science concepts using Python. With the help of interesting activities and hands-on coding exercises, you'll learn about data science, extended data analysis, linear and logistic regression, data visualization, k-means clustering, and decision trees.

Projects in Data Science

Janets

Learning & Development

Learning To Learn

Deep Learning

Deep Learning & Neural Networks Python - Keras

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This course is designed for existing Python programmers who have at least one year of Python experience and who want to expand their programming proficiency in Python 3.</p>Overview<p>In this course, you will expand your Python proficiencies. You will:<br />Select an object-oriented programming approach for Python applications.<br />Create object-oriented Python applications.<br />Create a desktop application.<br />Create data-driven applications.<br />Create and secure web service-connected applications.<br />Program Python for data science.<br />Implement unit testing and exception handling.<br />Package an application for distribution.</p><p>Python continues to be a popular programming language, perhaps owing to its easy learning curve, small code footprint, and versatility for business, web, and scientific uses. Python is useful for developing custom software tools, applications, web services, and cloud applications. In this course, you'll build upon your basic Python skills, learning more advanced topics such as object-oriented programming patterns, development of graphical user interfaces, data management, creating web service-connected apps, performing data science tasks, unit testing, and creating and installing packages and executable applications.</p></div><div id="course-pills-2"><h4>Lesson 1: Selecting an Object-Oriented Programming Approach for Python Applications</h4><ul><li>Topic A: Implement Object-Oriented Design</li><li>Topic B: Leverage the Benefits of Object-Oriented Programming</li></ul><h4>Lesson 2: Creating Object-Oriented Python Applications</h4><ul><li>Topic A: Create a Class</li><li>Topic B: Use Built-in Methods</li><li>Topic C: Implement the Factory Design Pattern</li></ul><h4>Lesson 3: Creating a Desktop Application</h4><ul><li>Topic A: Design a Graphical User Interface (GUI)</li><li>Topic B: Create Interactive Applications</li></ul><h4>Lesson 4: Creating Data-Driven Applications</h4><ul><li>Topic A: Connect to Data</li><li>Topic B: Store, Update, and Delete Data in a Database</li></ul><h4>Lesson 5: Creating and Securing a Web Service-Connected App</h4><ul><li>Topic A: Select a Network Application Protocol</li><li>Topic B: Create a RESTful Web Service</li><li>Topic C: Create a Web Service Client</li><li>Topic D: Secure Connected Applications</li></ul><h4>Lesson 6: Programming Python for Data Science</h4><ul><li>Topic A: Clean Data with Python</li><li>Topic B: Visualize Data with Python</li><li>Topic C: Perform Linear Regression with Machine Learning</li></ul><h4>Lesson 7: Implementing Unit Testing and Exception Handling</h4><ul><li>Topic A: Handle Exceptions</li><li>Topic B: Write a Unit Test</li><li>Topic C: Execute a Unit Test</li></ul><h4>Lesson 8: Packaging an Application for Distribution</h4><ul><li>Topic A: Create and Install a Package</li><li>Topic B: Generate Alternative Distribution Files</li></ul></div>

Continuous Professional Development (CPD)

Advanced Programming Techniques with Python (v1.1)

<p>Become a data-driven professional with this comprehensive bundle course!</p><p>Master the in-demand skills to extract valuable insights from data and transform them into clear, actionable stories. This course equips you with the proficiency to thrive in today's data-centric world, preparing you for exciting careers in data analysis and beyond.</p><p>This bundle isn't your average data analytics training. We'll equip you to go beyond number crunching and become a data whisperer, someone who can coax valuable insights from even the most cryptic datasets.</p><p><strong>Uncover Hidden Stories in Your Data:</strong></p><ul><li>Master the art of data wrangling - tame messy data and transform it into a clear, actionable narrative.</li><li>Leverage the power of Python, the secret weapon of data scientists, to unlock the full potential of your information.</li><li>Craft stunning data visualisations with Power BI and Tableau, turning complex data into captivating stories that resonate with any audience.</li><li>Demystify SQL, the key to unlocking the treasure trove of information hidden within databases.</li><li>Master Excel Formula VLOOKUP in 60 minutes! With VLOOKUP you can only look for values vertically the course will also teach you the trick to look for data from left to right and 2-way lookups.</li></ul><p>These in-depth courses will hone your skills and transform you into a data whisperer!</p><p><strong>Data Analytics </strong></p><ul><li><strong>From Chaos to Clarity:</strong> This course isn't just about data analysis - it's about data wrangling. You'll learn how to transform messy datasets into a clear and actionable story and make data-driven decisions with confidence.</li></ul><p><strong>Python </strong></p><ul><li><strong>Python Power Up:</strong> Unleash the true potential of your data with Python, the go-to language for data scientists. This course will equip you with the fundamentals to clean, analyse, and manipulate data with ease, giving you the edge in today's data-driven world.</li></ul><p><strong>Power BI </strong></p><ul><li><strong>From Static Reports to Dynamic Dashboards:</strong> Go beyond static reports and create interactive dashboards with Power BI. This course will guide you from data connection to crafting compelling visualisations that bring your data to life and engage your audience.</li></ul><p><strong>Tableau </strong></p><ul><li><strong>Data with Impact:</strong> Transform data into captivating narratives with Tableau. This course will equip you with the skills to create clear and impactful data visualisations, allowing you to communicate complex insights in a way that resonates with any audience.</li></ul><p><strong>SQL </strong></p><ul><li><strong>Beyond the Surface: Dive Deep into Databases:</strong> SQL is the key to unlocking the hidden treasures within databases. This course will empower you to write complex queries and extract specific information, giving you complete control over your data.</li></ul><p><strong>VLOOKUP </strong></p><ul><li><strong>Level Up Your Data Retrieval Game:</strong> VLOOKUP may be a familiar friend, but this course equips you with the skills to become a data retrieval ninja. You will learn how to use VLOOKUP for two-way data retrieval, perfect for scenarios where your lookup value isn't in the first column. Discover valuable tips and tricks to optimize your VLOOKUP usage and troubleshoot common issues effectively.</li></ul><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This isn't just for aspiring data analysts - it's for anyone who wants to transform data into a strategic advantage.</p><p>This course is designed for a broad audience seeking to unlock the power of data:</p><ul><li>Individuals with no prior experience</li><li>Professionals seeking to upskill</li><li>Anyone passionate about data</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p><strong>No prior experience? No problem!</strong> We'll guide you step-by-step, from data novice to data whisperer.</p><ul><li>Basic computer literacy and internet access are all you need to get started.</li><li>No prior programming experience is required for the Python course. The curriculum is designed to be beginner-friendly, guiding you step-by-step.</li></ul>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This comprehensive bundle course prepares you for various data analyst positions across industries. Upon completion, you'll be well-equipped to pursue exciting careers such as:</p><ul><li>Business Intelligence Analyst</li><li>Marketing Analyst</li><li>Financial Analyst</li><li>Data Scientist (with further studies)</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certification of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div><p>Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate. </p></div>
        <h4>Certification of completion </h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div><p>Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery). </p><p><br /><br /></p></div>

                        </div>

One Education

Data Analyst: Data Analytics + Visualisation with Python, Power BI, Tableau, SQL & VLOOKUP Course

London

<article>
<div>
<h5>Java EVENINGS Course</h5>
</div>
<div></div>
<p>  						 
	</p><div>
	<ul>
		<p>
		<b>Python EVENINGS Course. </b>Basic Python syntax and principles of Object Orientated Programming. 
		<br />Most attendees are in-work IT Professional.
		<br />Private individuals are also very welcome. 
		<br />Evening courses also running.
		<br /><b>Our Style: </b>Hands-on, Practical  
		<br /><b>Location:  </b>Online, Instructor-led  
		<br /><b>Download:  </b>anaconda.com
		<br /><b>Duration:  </b>6 weeks, 1 evening per week, 6pm - 8pm
		<br /><b>Times: </b>arrange a time for your time zone
	    </p>
	</ul>	

<p></p>
</div></article>

<div>
	  <div>
		 <h5>Python Basics EVENINGS Course </h5>
	  </div>
	  <div></div>
	  <p> 
 
				This Python course London Evening covers the essential Python Basics, in our interactive, instructor led Live Virtual Classroom.
				This Python Basics course is a very good introduction to essential fundamental programming concepts using Python as programming language.
				These concepts are daily used by programmers and is your first step to working as a programmer.
				By the end, you'll be comfortable in programming Python code.
				You will have done small projects.
				This will serve for you as examples and samples that you can use to build larger projects.
				You can publish your code online to keep as a portfolio.

<br />
This Python Programming course is hands-on and practical, to give you in-depth, practical experience of the topics covered.

	  </p>
	<div>
	<h5>Week 1: Basics</h5>
	</div>
	<div></div>
	<div> 
					<p>  <strong>Python Data Types and Variables:</strong>  
					<br />  Primitive types; Characters &amp; Strings; Boolean; Working with variables and its scope; Conversion and casting types in Python.	 
					<br /> <strong>Operators and Expressions: </strong>   
					<br />  Introduction of operators; Arithmetic operators; Relational operators; Assignment operator; Logical operators; Increment and decrement operators.
					</p> 
			 
				<p><strong> Decision Making:</strong>
				<br />If statement; If - else statement; If- elif - else statement; Nested if - else.
				</p><p><strong>Using Loops:</strong>
				<br />The while, while-else.
				<br />For loop;
				<br />Jump statements: break, continue;
				<br />Nesting loops.				
				</p>
			</div>
	<div>
	<h5>Week 2: Data Structures</h5>
	</div> <div></div>
	<div> 
				<p> Lists.
					<br />Tuples.
					<br />Sets.
					<br />Dictionary. Nested Dictionary.
					<br />Json Files.
				</p>
			</div>
	<div>
	<h5>Week 3: Python built-in functions</h5>
	</div> <div></div>
	<div> 
				<p>  
				<br />Using Built-in modules and functions:
				<br />Strings Functions.
				<br />Pattern matching with Regex.
				<br />Maths Functions.
				<br />Random Functions.
				<br />Python Date functions.
				</p>
			</div>
	<div>
	<h5>Week 4: OOP: Using Modules, functions, classes</h5>
	</div> <div></div>
	<div>
				<p>  Creating and using custom Functions.
					<br />Creating functions;
					<br />Using parameters and return values;
					<br />
					<br />Introduction to Classes and Objects:
					<br />Creating a Class; 
					<br />Creating an Object; Using an Object; 
					<br />Adding Instance variables; Controlling accessibility; Naming conventions for class members. 
					<br /> Class Constructors; Parameterized Constructors.
					<br />Inheritance. Overload.
				</p>
			</div>

	<div>
	<h5>Week 5: Pandas DataBase Basics</h5>
	</div> <div></div>
	<div>	 
		<p> <b>Connection details:</b>
				Connect to a database using access codes. 
				<br /><b>Select Queries:</b>
				Retrieving records and iterating results sets, using variables and parameters in queries. 
				<br />Working with DDL comands:
				Create a database, drop a database.
				Create a table, alter tables, drop a table. 
				<br />Working with DML Commands:
				Insert, delete, update records.
		</p>
	</div>
	
	 
	<div>
	<h5>Week 6: Pandas Dataframe Basics</h5>
	</div> <div></div>
	<div>	 
		<p> Getting data into a dataframe:
			<br />Dict to Dataframe, Dataframe to Dict.
			<br />Excel To Dict, Dict to Excel , working with Excel data, multiple Excel sheets.

			<br />Dataframe basics:
			<br />Slicing Dataframes,
			<br />Getting information about the dataframe,
			<br />Filter, sort and query a Dataframes,
			<br />Duplicate values,
			<br />Working with null-values,
			<br />Sampling,
			<br />Group by,
			<br />Join.

		</p>
	</div>
	  
</div>
 

<div>   

	   <div id="cincluded">
		  <div>
			 <h5>What is included</h5>
		  </div>

		  <article>
		  <div>
			 <ul>
				<li>PCWorkshops Course Certificate on completion  </li>
									   
				<li>Python Course Notes</li>
				<li>Python Code Examples</li>
				<li>Practical Python Course exercises and Code Samples</li>
				<li>After the course: 1-Hour personalised online revision session</li>
			 </ul>
		  </div>
		  </article>
	   </div>
				    
<div>
	                  <div>
	                     <h5 id="cincluded2">Book the Python  Course</h5>
	                  </div>
 
<div id="eventbrite-widget-container-60636103217"></div>




							 
</div>
				   <div id="caboutus">
	                  <div>
	                     <h5>About us</h5>
	                  </div> 
	                  	<article>
					    <div>
					    	<div>
					    		<div>
					    			
					    			<h5>Our experienced trainers are award winners. </h5> 
					    			<ul>
										<li>
											More about us
										</li>
										<li>
											FAQ's
										</li>
										<li>
											Client Comments  
										</li>
									 </ul>
					    		</div>
					    		<div>
					    			
					    				
					    			
					    		</div>
					    	</div>
	                     
					    </div>
					    </article>						
	               </div>
				   
				   
	        </div>

Python EVENINGS Course
Basic Python syntax and principles of object-oriented programming. 
Most attendees are in-work IT Professional. 
Private individuals are also very welcome. 
Evening courses also running. 

Details:
Our Style: Hands-on, Practical 
Location: Online, Instructor-led 
Download: anaconda.com 
Duration: 4 weeks, 1 evening per week, 6pm - 8pm 
Times: arrange a time for your time zone 
How to attend:  
Connection Link For the course: https://meet.goto.com/910697125
Once you have made the payment, use the above link on the date booked, to connect with the trainer. 
Python Basics EVENINGS Course
 This Python course London Evening covers the essential Python Basics, in our interactive, instructor led Live Virtual Classroom. This Python Basics course is a very good introduction to essential fundamental programming concepts using Python as programming language. These concepts are daily used by programmers and is your first step to working as a programmer. By the end, you'll be comfortable in programming Python code. You will have done small projects. This will serve for you as examples and samples that you can use to build larger projects. You can publish your code online to keep as a portfolio. 

This Python Programming course is hands-on and practical, to give you in-depth, practical experience of the topics covered. 
Week 1: Basics
 Python Data Types and Variables: 
Primitive Data types; Working with variables and its scope; Conversion and casting types in Python. 
 Operators and Expressions:  
 Introduction of operators; Arithmetic operators; Relational operators; Assignment operator; Logical operators; Increment and decrement operators. 
 Decision Making: 
If statement; If - else statement; If- elif - else statement; Nested if - else. 
Using Loops: 
The while, while-else. 
For loop; 
Jump statements: break, continue; 
Nesting loops. 
Week 2: Data Structures & Database
Lists.  
Tuples. 
Sets. 
Dictionary. Nested Dictionary. 
Json Files. 
Reading csv and text files
Connecting with a database and run SQL Queries. 
Week 3: Python built-in functions &  Pandas Dataframe Basics
 
Using built-in modules and functions: 
Strings Functions. 
Maths Functions. 
Random Functions. 
Data Frames; 
Getting data into a dataframe: 
Dict to Dataframe, Dataframe to Dict.  
Dataframe basics: 
Slicing Dataframes, 
Getting information about the dataframe, 
Filter and query a Dataframes, 
Working with null-values.
Week 4: OOP: Using Modules, functions, classes
 Creating and using custom Functions. 
Creating functions; 
Using parameters and return values; 
 
Introduction to Classes and Objects: 
Creating a Class; 
Creating an Object; Using an Object; 
Adding instance variables; controlling accessibility; and naming conventions for class members. 
 Class Constructors; Parameterized Constructors. 
Inheritance. Overload. 

What is included
PCWorkshops Course Certificate on completion
Python Course Notes
Python Code Examples
Practical Python Course exercises and Code Samples
After the course: 1-Hour personalised online revision session
  

PCWorkshops

Instructor

Basic Python syntax and principles of Object Orientated Programming. 
Most attendees are in-work IT Professional. 
Private individuals are also very welcome. 
Evening courses also running. 
Our Style: Hands-on, Practical 
Location: Online, Instructor-led 
Download: anaconda.com 
Duration: 6 weeks, 1 evening per week, 6pm - 8pm 
Times: arrange a time for your time zone 

Python EVENINGS Course, Online Instructor-led

General admission

<article>
					<div>
						<h5>Python Beginners Course, 1-Day.</h5>
					</div>
					<div></div>
					<p>
						<span> </span>
						<br />This course covers the essential Python Basics, in our interactive, instructor led Live Virtual Classroom.
						<br />	This Python Basics course is a very good introduction to essential fundamental programming concepts using Python as programming language.
							<br />These concepts are daily used by programmers and is your first step to working as a programmer.
							<br />By the end, you'll be comfortable in programming Python code.
							<br />You will have done small projects.
							<br />This will serve for you as examples and samples that you can use to build larger projects.
						 
						<br /><b>Location: </b>Instructor-led, Online.
						<br /><b>Our Style: </b>Hands-on, Practical Course.
						<br /><b>Group Size: </b>Max 4 people per group.  
						<br /><b>Qualification: </b>PCWorkshops Beginners Certification
						<br /><b>Hours: </b>1 Day, 10am-5pm
						<br /><b>Download: </b>anaconda.com
						
				  	</p>
	    		</article> 
				
				<article>
					<div>
						<h5>Python Beginners Course, Course Outline.</h5>
					</div>
					<div></div>
					<p>
						<span> </span>
						<br />
						<b>Session 1: Basics</b>
 
						<br />Python Data Types and Variables:
						Working with variables and its scope; Conversion and casting types in Python.
						<br />Operators and Expressions.
						 

						<br />Decision Making: If statement; If - else statement; If- elif - else statement; Nested if - else. 
						
						<br />Using Loops: The while, while-else. For loop; Jump statements: break, continue; Nesting loops.
<br />
						<br /><b>Session 2: Data Structures</b>
						 
						<br />Lists.
						<br />Tuples.
						<br />Sets.
						<br />Dictionary. Nested Dictionary.
						<br />Json Files.
<br />
						<br /><b>Session 3: Python Built-in functions</b> 
						<br />Strings Functions. 
						<br />Maths Functions.
						<br />Random Functions.
						<br />Python Date functions.
<br />
						<br /><b>Session 4: OOP: Using Modules, functions, classes</b>
						 
						<br />Creating and using custom Functions.
						
						<br />Using parameters and return values.

						<br />Introduction to Classes and Objects:
						<br />Creating a Class. Creating and using Objects. 
						<br />Class Constructors.
						<br />Inheritance. Overload.
<br />
						<br /><b>Session 5: Access SQL Database Via Python</b>
						 
						<br /><b>Connection details:</b> Connect to a database using access codes.
						<br /><b>Select Queries:</b> Retrieving records and iterating results sets, using variables and parameters in queries.
						<br /><b>Working with DDL comands: </b>Create a database, drop a database. Create a table, alter tables, drop a table.
						<br /><b>Working with DML Commands: </b>Insert, delete, update records.

						<br /><br /><br /><b>Session 6: Pandas Dataframe Basics</b>
						 
						<br />Getting data into a dataframe:
						<br />Dict to Dataframe, Dataframe to Dict.
						<br />Excel To Dict, Dict to Excel , working with Excel data, multiple Excel sheets.
						<br />Dataframe basics:
						<br />Slicing Dataframes, Querying, filter and sort a dataframe,
						
						<br />Working with duplicates and null-values, Sampling, Group by, Join.
						
						
						
				  	</p>
	    		</article> 

			    <div>  
				     
	     
	               <div id="cincluded">
	                  <div>
	                     <h5>Python Beginners Course Materials</h5>
	                  </div>

	                  <article>
	                  <div>
	                     <ul>
	                        <li>Python Beginners Certificate on completion  </li>
							                  
						    <li>Python Beginners Notes</li>
							<li>Python Code Examples</li>
	                        <li>Practical Python Beginners exercises</li>
	                        
	                     </ul>
	                  </div>
						   
	                  </article>
	               </div>
			
				    <article>
					<div>
						<h5>More about Python</h5>
					</div>
					<div></div>
					<p>
							Python is the Easiest Programming Language to Learn 
				 				Anybody can learn how to use it and  entry-level coders get access to the industry in a 
									fast-tacked manner and can from there migrate to other languages.
								 <br />
								 Learning to code  is now more important than ever, since a growing number of application and <br />
								  digital products require programmers. <br />

								 Additionally, as an interpreted coding language, Python allows you to run each line of code <br />
								 after writing it, rather 
								 than other compiled languages, such as C++. <br />
								 This is particularly useful for beginners, as they can test their work and 
								  gain instant feedback, <br />
								  to make amendments where required and continuously improve. A Python Course will
								explain to you how to do this<br />
								  <br />
								 Python is a Versatile Coding Language <br />
								 Use it for : <br /> 
								 Data mining ,  Data analytics , Data visualisation ,  AI and machine learning,  Building apps ,
								 Web development and web application development ,  Developing games,  Quantitative and qualitative analysis,
								 Data Engineering,  A Python Course is the start of all of the above.
								 <br /><br />
								 Learning Python will Improve Your Job Prospects <br />
								 As the fastest-growing computer programming language in the world, 
								 Python is also one of the most in-demand jobs. According to 2022 LinkedIn data,   
								Python is the second most in-demand programming language, after Java.  
								All of this makes a Python Course essential. 
				  	</p>
	    		</article> 
					  
				    
					
					
					
				   <div id="booking">
	                  <div>
	                     <h5>Book the Python Beginners Course</h5>
	                  </div>
							 
 							<div id="eventbrite-widget-container-48024956939"></div>

							

							
							 
	               </div>
				   <div id="cincluded">
	                  <div>
	                     <h5>About us</h5>
	                  </div> 
	                  	<article>
					    <div>
					    	<div>
					    		<div>
					    			
					    			<h5>Our experienced trainers are award winners. </h5> 
					    			<ul>
										<li>
											More about us
										</li>
										<li>
											FAQ's
										</li>
										<li>
											Client Comments  
										</li>
									 </ul>
					    		</div>
					    		<div>
					    			
					    				
					    			
					    		</div>
					    	</div>
	                     
					    </div>
					    </article>						
	               </div>
				   
				   
	            </div>

Python Beginners Course, 1-Day.
   
This course covers the essential Python Basics, in our interactive, instructor led Live Virtual Classroom. 
	This Python Basics course is a very good introduction to essential fundamental programming concepts using Python as programming language. 
These concepts are daily used by programmers and is your first step to working as a programmer. 
By the end, you'll be comfortable in programming Python code. 
You will have done small projects. 
This will serve for you as examples and samples that you can use to build larger projects. 

Location: Instructor-led, Online. 
Our Style: Hands-on, Practical Course. 
Group Size: Max 4 people per group. 
Qualification: PCWorkshops Beginners Certification 
Hours: 1 Day, 10am-5pm 
Download: anaconda.com 
Python Beginners Course, Course Outline.
  Session 1: Basics 
Python Data Types and Variables: Working with variables and its scope; Conversion and casting types in Python. 
Operators and Expressions. 
Decision Making: If statement; If - else statement; If- elif - else statement; Nested if - else. 
Using Loops: The while, while-else. For loop; Jump statements: break, continue; Nesting loops.

 
Session 2: Data Structures 
Lists. 
Tuples. 
Sets. 
Dictionary. Nested Dictionary. 
Json Files.

 
Session 3: Python Built-in functions 
Strings Functions. 
Maths Functions. 
Random Functions. 
Python Date functions.

 
Session 4: OOP: Using Modules, functions, classes 
Creating and using custom Functions. 
Using parameters and return values. 
Introduction to Classes and Objects: 
Creating a Class. Creating and using Objects. 
Class Constructors. 
Inheritance. Overload.

 
Session 5: Access SQL Database Via Python 
Connection details: Connect to a database using access codes. 
Select Queries: Retrieving records and iterating results sets, using variables and parameters in queries. 
Working with DDL comands: Create a database, drop a database. Create a table, alter tables, drop a table. 
Working with DML Commands: Insert, delete, update records. 


Session 6: Pandas Dataframe Basics 
Getting data into a dataframe: 
Dict to Dataframe, Dataframe to Dict. 
Excel To Dict, Dict to Excel , working with Excel data, multiple Excel sheets. 
Dataframe basics: 
Slicing Dataframes, Querying, filter and sort a dataframe, 
Working with duplicates and null-values, Sampling, Group by, Join. 
Python Beginners Course Materials
Python Beginners Certificate on completion
Python Beginners Notes
Python Code Examples
Practical Python Beginners exercises
More about Python
 Python is the Easiest Programming Language to Learn Anybody can learn how to use it and entry-level coders get access to the industry in a fast-tacked manner and can from there migrate to other languages. 
 Learning to code is now more important than ever, since a growing number of application and 
 digital products require programmers. 
 Additionally, as an interpreted coding language, Python allows you to run each line of code 
 after writing it, rather than other compiled languages, such as C++. 
 This is particularly useful for beginners, as they can test their work and gain instant feedback, 
 to make amendments where required and continuously improve. A Python Course will explain to you how to do this
 
 Python is a Versatile Coding Language 
 Use it for : 
 Data mining , Data analytics , Data visualisation , AI and machine learning, Building apps , Web development and web application development , Developing games, Quantitative and qualitative analysis, Data Engineering, A Python Course is the start of all of the above. 

 Learning Python will Improve Your Job Prospects 
 As the fastest-growing computer programming language in the world, Python is also one of the most in-demand jobs. According to 2022 LinkedIn data, Python is the second most in-demand programming language, after Java. All of this makes a Python Course essential. 
 

Programming Languages

Computer Skills

his course covers the essential Python Basics, in our interactive, instructor led Live Virtual Classroom. 
	This Python Basics course is a very good introduction to essential fundamental programming concepts using Python as programming language. 
These concepts are daily used by programmers and is your first step to working as a programmer. 
By the end, you'll be comfortable in programming Python code. 
You will have done small projects. 
This will serve for you as examples and samples that you can use to build larger projects. 

Python Beginners Course, 1-Day

<div style="color: rgb(57, 54, 79); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(57, 54, 79);"><div data-testid="summary" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><div data-testid="summary" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><div data-testid="summary" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This Python BootCamp is Instructor-led, Practical. In the12-week Python course, learn start to in-depth, leading to a good Python career.</strong></p></div><div data-testid="organizerBrief" fr-original-style="" style="box-sizing: border-box;"><div data-testid="creator-signal-main-section" fr-original-style="" style="box-sizing: border-box;"><section fr-original-style="" style="box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></section></div></div></div><div data-testid="organizerBrief" fr-original-style="" style="box-sizing: border-box;"><div data-testid="creator-signal-main-section" fr-original-style="" style="box-sizing: border-box;"><section fr-original-style="" style="box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></section></div></div></div><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><div style="color: rgb(57, 54, 79); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(57, 54, 79);"><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">PYTHON BOOTCAMP:</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This 12-week Python Boot Camp is a practical, instructor-lead program, covering Python from start to in-depth. You will be fully fluent and confident as a Python programmer.</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">If you have more questions goto&nbsp;<a href="http://pcworkshopslondon.co.uk/contact.html" target="_blank" rel="nofollow noopener noreferrer" data-airgap-id="66" style="color: var(--eds-control,#3659e3); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: var(--eds-control,#3659e3);">https://pcworkshopslondon.co.uk/contact.html&nbsp;</a>,</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Or&nbsp;<a href="https://pcworkshopslondon.co.uk/" target="_blank" rel="nofollow noopener noreferrer" data-airgap-id="67" style="color: var(--eds-control,#3659e3); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: var(--eds-control,#3659e3);">contact us on training@pcworkshopslondon.co.uk</a></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><a href="https://pcworkshopslondon.co.uk/contact.htm" target="_blank" rel="nofollow noopener noreferrer" data-airgap-id="68" style="color: var(--eds-control,#3659e3); user-select: auto; font-size: var(--font-size); box-sizing: border-box; cursor: pointer; text-decoration: inherit;" fr-original-style=" color: var(--eds-control,#3659e3);">C﻿ustomise dates, course outline, arrange installments</a></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">This course will give you enough&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">practical experience and practical projects</strong>&nbsp;to code, to give you full confidence to enter into the coding profession. &nbsp;&nbsp;</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Duration:&nbsp;</strong>3 months:</p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">1 Python class per week,</li><li fr-original-style="" style="box-sizing: border-box;">Plus pratical work,</li><li fr-original-style="" style="box-sizing: border-box;">Plus personal trainer-mentor for 1-1 training,</li><li fr-original-style="" style="box-sizing: border-box;">Plus e-learning materials.</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Final project :&nbsp;</strong>Practical to upload to&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">GitHub&nbsp;</strong>and show-case</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Date and times, choose:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Fridays in London or Online&nbsp;</strong>, 10am - 5pm,</li><li fr-original-style="" style="box-sizing: border-box;">or&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Saturdays in London or Online&nbsp;</strong>, 12noon-6pm,</li><li fr-original-style="" style="box-sizing: border-box;">o﻿r negotiate your date</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Study level:</strong> Start from beginners level to in-depth, ready to work professionally.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Virtual attendance: &nbsp;</strong>online instructor-led&nbsp;</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Download</strong>: Anaconda.com</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Pre-requisites:&nbsp;</strong>General computer literacy.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Oracle Qualification:&nbsp;</strong>PCWorkshops Python Programmer Certificate</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Payments:</strong>&nbsp; You may apply to pay in installments for this Python Training course</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">COURSE OUTLINE</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">W﻿eek 1 - 4: Essentials</strong></p><ol fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Python Coding Basics</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Object Oriented programming:&nbsp;</strong>Python Object Orientated programming (OOP)</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">UX Principles:&nbsp;</strong>UX Principles and applying it on Front-ends with TKinter</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Specialised topics:&nbsp;</strong>Dates, Localization, Strings, Maths Operation , Random Number,&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Lambdas</strong></li></ol><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">W﻿eek 5 - 10 : All about data</strong></p><ol fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Data Structures:</strong>&nbsp;Lists, Tuples, Sets,&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Exceptions</strong>, I/O Fundamentals , Reading and Writing file</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Database:&nbsp;</strong>Database principles and SQL.&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Database Project:&nbsp;</strong>Python database connections and creating a database driven project</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Analytics:&nbsp;</strong>Numpy. Pandas for data analytics and data queries.</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Analytics:&nbsp;</strong>Pandas data cleaning and restructuring, interacticting with Excel, Csv, Json,etc.</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data visualisation:&nbsp;</strong>MatPlotLib</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Prediction:&nbsp;</strong>Machine Learning Basics</li></ol><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">W﻿eek 11 and 12: The final touch</strong></p><ol fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Concurrency and Multi-threading:</strong>&nbsp;Threads vs. Processes, Threading Module, Threading Event, Stop a Thread, Daemon Threads, Thread-safe Queue, Thread Pools, Locks</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Unit Testing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">O﻿ptional&nbsp;</strong>: Replace Week 11 or 12 with Scraping using Python, ot Tkinter Front-ends</li></ol><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">I﻿NCLUDED:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">PCWorkshops Python Course Certificate on completion.</li><li fr-original-style="" style="box-sizing: border-box;">Python Course notes.</li><li fr-original-style="" style="box-sizing: border-box;">Practical Course exercises, Code Examples, Online Materials.</li><li fr-original-style="" style="box-sizing: border-box;">After the course, continuous assistance with practical and preparation for exams</li><li fr-original-style="" style="box-sizing: border-box;">Max group size on this is 4.</li><li fr-original-style="" style="box-sizing: border-box;">Portfolio: Post your Python project online.</li><li fr-original-style="" style="box-sizing: border-box;">Exam preparation and interview questions</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">MORE ABOUT THE ONLINE CLASSROOM</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">&nbsp;Attend from your internet connection</li><li fr-original-style="" style="box-sizing: border-box;">&nbsp;Instructor-led, get instant answers to your questions</li><li fr-original-style="" style="box-sizing: border-box;">&nbsp;Fully interactive</li><li fr-original-style="" style="box-sizing: border-box;">&nbsp;Work clearly explained with demonstrations and examples</li><li fr-original-style="" style="box-sizing: border-box;">&nbsp;Practical exercises to be tried out by delegate</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">WHAT YOU WILL GAIN:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Skills &amp; knowledge: Python coding knowledge and understanding with good practical application &nbsp;</li><li fr-original-style="" style="box-sizing: border-box;">Certification:&nbsp;Internal PCWorkshops Python certificate</li><li fr-original-style="" style="box-sizing: border-box;">Portfolio: You will have an online portfolio of Python projects&nbsp;</li><li fr-original-style="" style="box-sizing: border-box;">Experience:&nbsp;Our comprehensive practical makes you job ready</li></ul></div><div data-testid="image-content" style="color: rgb(57, 54, 79); box-sizing: border-box;" fr-original-style=" color: rgb(57, 54, 79);"><img src="https://img.evbuc.com/https%3A%2F%2Fcdn.evbuc.com%2Fimages%2F444528299%2F44980801875%2F1%2Foriginal.20230212-111230?h=2000&w=720&auto=format%2Ccompress&q=75&sharp=10&s=bc03bf9766e6c9ca0436ac60d0be83d6" alt="" data-airgap-id="88" fr-original-style="" style="cursor: pointer; padding: 0px 1px; user-select: none; max-width: 100%; position: relative; box-sizing: border-box;" fr-original-class="fr-draggable"></div><div data-testid="image-content" style="color: rgb(57, 54, 79); box-sizing: border-box;" fr-original-style=" color: rgb(57, 54, 79);"><img src="https://img.evbuc.com/https%3A%2F%2Fcdn.evbuc.com%2Fimages%2F588336959%2F44980801875%2F1%2Foriginal.20230901-170557?h=2000&w=720&auto=format%2Ccompress&q=75&sharp=10&s=e4036ff76e1af36cfba9452461b16c48" alt="" data-airgap-id="87" fr-original-style="" style="cursor: pointer; padding: 0px 1px; user-select: none; max-width: 100%; position: relative; box-sizing: border-box;" fr-original-class="fr-draggable"></div><div data-testid="image-content" style="color: rgb(57, 54, 79); box-sizing: border-box;" fr-original-style=" color: rgb(57, 54, 79);"><img src="https://img.evbuc.com/https%3A%2F%2Fcdn.evbuc.com%2Fimages%2F588337149%2F44980801875%2F1%2Foriginal.20230901-170614?h=2000&w=720&auto=format%2Ccompress&q=75&sharp=10&s=cdf1fcea5ad47abc354df67b458d5237" alt="" data-airgap-id="89" fr-original-style="" style="cursor: pointer; padding: 0px 1px; user-select: none; max-width: 100%; position: relative; box-sizing: border-box;" fr-original-class="fr-draggable"></div><div data-testid="image-content" style="color: rgb(57, 54, 79); box-sizing: border-box;" fr-original-style=" color: rgb(57, 54, 79);"><img src="https://img.evbuc.com/https%3A%2F%2Fcdn.evbuc.com%2Fimages%2F588337349%2F44980801875%2F1%2Foriginal.20230901-170630?h=2000&w=720&auto=format%2Ccompress&q=75&sharp=10&s=839d02fa3087d55ac858bcbb697972f5" alt="" data-airgap-id="86" fr-original-style="" style="cursor: pointer; padding: 0px 1px; user-select: none; max-width: 100%; position: relative; box-sizing: border-box;" fr-original-class="fr-draggable"></div></div><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></p><div style="color: rgb(30, 10, 60); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(30, 10, 60);"><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><h2 fr-original-style="" style="box-sizing: border-box;">Refund Policy</h2></div><div style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">No Refunds</div>

This Python Data Analytics BootCamp is Instructor-led, Practical. In the12-week Python course, learn start to in-depth, leading to a good Python career.



PYTHON BOOTCAMP:
This 12-week Python Data Analytics Data Boot Camp is designed to give you a complete skill set required by data analysts .
You will be fully fluent and confident as a Python  data analyst, with full understanding of Python Programming.
From Data, databases, datasets, importing, cleaning, transforming, analysing to visualisation and creating awesome dashboards
The course is a practical, instructor-lead program.

If you have more questions goto https://pcworkshopslondon.co.uk/contact.html,
Or contact us on training@pcworkshopslondon.co.uk
C﻿ustomise dates, course outline, arrange installments
This course will give you enough practical experience and practical projects to code, to give you full confidence to enter into the coding profession. 
Duration: 3 months, 12 weeks:
1 Python class per week,
Plus practical work,
Plus personal trainer-mentor for 1-1 training,
Plus e-learning materials.
Final project : Practical to upload to GitHub and show-case
Date and times, choose:
Fridays in London or Online , 10am - 5pm,
or Saturdays in London or Online , 12noon-6pm,
o﻿r negotiate your date
Study level: Start from beginners level to in-depth, ready to work professionally.
Virtual attendance: online instructor-led 
Download: Anaconda.com
Pre-requisites: General computer literacy.
Oracle Qualification: PCWorkshops Python Programmer Certificate
Payments: You may apply to pay in installments for this Python Training course
COURSE OUTLINE
W﻿eek 1 - 4: Essentials
Python Coding Basics
Object Oriented programming: Python Object Orientated programming (OOP)
UX Principles: UX Principles and applying it on Front-ends with TKinter
Specialised topics: Dates, Localization, Strings, Maths Operation , Random Number, Lambdas
W﻿eek 5 - 10 : All about data
Python Data Structures: Lists, Tuples, Sets, Exceptions, I/O Fundamentals , Reading and Writing file
Database: Database principles and SQL. Database Project: Python database connections and creating a database driven project
Data Analytics: Numpy. Pandas for data analytics and data queries.
Data Analytics: Pandas data cleaning and restructuring, interacticting with Excel, Csv, Json,etc.
Data visualisation: MatPlotLib
Prediction: Machine Learning Basics
W﻿eek 11 and 12: The final touch
Python Concurrency and Multi-threading: Threads vs. Processes, Threading Module, Threading Event, Stop a Thread, Daemon Threads, Thread-safe Queue, Thread Pools, Locks
Python Unit Testing
O﻿ptional : Replace Week 11 or 12 with Scraping using Python, ot Tkinter Front-ends
I﻿NCLUDED:
PCWorkshops Python Course Certificate on completion.
Python Course notes.
Practical Course exercises, Code Examples, Online Materials.
After the course, continuous assistance with practical and preparation for exams
Max group size on this is 4.
Portfolio: Post your Python project online.
Exam preparation and interview questions
MORE ABOUT THE ONLINE CLASSROOM
Attend from your internet connection
Instructor-led, get instant answers to your questions
Fully interactive
Work clearly explained with demonstrations and examples
Practical exercises to be tried out by delegate
WHAT YOU WILL GAIN:
Skills & knowledge: Python coding knowledge and understanding with good practical application
Certification: Internal PCWorkshops Python certificate
Portfolio: You will have an online portfolio of Python projects
Experience: Our comprehensive practical makes you job ready






Refund Policy
No Refunds

PYTHON BOOTCAMP:



This 12-week Python Data Analytics Data Boot Camp is designed to give you a complete skill set required by data analysts .



You will be fully fluent and confident as a Python  data analyst, with full understanding of Python Programming.



From Data, databases, datasets, importing, cleaning, transforming, analysing to visualisation and creating awesome dashboards



The course is a practical, instructor-lead program.



Python Data Analytics BootCamp, 12-weeks, Online Instructor-led

Python Bootcamp Saturdays, Online

<article>
<div>
<h5>Python Data Analytics Course</h5>
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    <div id="widget-reviews"><div><h3>Python Beginners Course</h3><div><span></span><span></span><span></span><span></span><span></span><div>(4.6)</div></div><span></span><br /><div>Based on 5 reviews</div><div><div><div id="previous"><i></i></div><div id="currentReview"><div> Excellent course, led by a knowledg</div><div> Excellent course, led by a knowledgeable and engaging instructor. I attended ...</div><div> Helen S</div></div><div id="next"><i></i></div></div></div></div></div>
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		<p>
		<b>Python Data Course. </b>Data Analytics with Python using Numpy, Pandas, Dataframes. 
		<br />Most attendees are in-work Data Professional.
		<br />Private individuals are very welcome. 
		<br /><b>Our Style: </b>Hands-on, Practical  
		<br /><b>Location:  </b>Online, Instructor-led  
		<br /><b>Download:  </b>anaconda.com
		<br /><b>Duration:  </b>2 consecutive days, 10am - 5pm
		<br /><b>Times: </b>arrange a time for your time zone
	    </p>
	</ul>	

<p></p>
</div></article>

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	  <div>
			<h5>Python Data Analytics Course </h5>
	  </div>
	  <div></div>
	  <p>  
	  <br /> 
	  </p>
	<div>
	<h5>Session 1: Database: </h5>
	</div>
	<div></div>
	<div> 
					<p>  <strong>SQLite3:</strong>  
					<br /> Linking with SQL Database.
					<br /> Create, Amend and Drop Tables.
					<br /> Insert, Update and delete records.
					<br /> Select queries.
					<br /> Traverse and display query results.
					<br /> Using parameters in queries.
					<br /> Get metadata about tables and columns. 
					</p> 
			</div>
	<div>
	<h5>Session 2: Data Structures</h5>
	</div> <div></div>
	<div> 
				<p> <strong>Data Structures:</strong>  
					<br />Tuples.
					<br />Sets.
					<br />Dictionary. Nested Dictionary.
					<br />Json Files.
				</p>
			</div>
	<div>
	<h5>Session 3: Python Numpy</h5>
	</div> <div></div>
	<div> 
				<p>  <strong>Numpy:</strong>  
				<br />The Python NumPy Module:
				<br />Working with arrays, create data using arrays.
				<br />Array manipulation.
				<br />String functions on arrays. 
				<br />Math, arithmetic and statistical functions.
				
				</p>
			</div>
	<div>
	<h5>Session 4: Python Pandas Dataframes and data importing</h5>
	</div> <div></div>
	<div>
		<p>  <strong>Python Dataframes</strong>  
		<br /> Data Series. Date/ Time Functionality. Time series.
		<br /> Creating Dataframes, Indexing. 
		<br />Dict to Dataframe, Dataframe to Dict.
		<br />Csv to Dataframe, Dataframe to csv.
		<br />Excel to Dataframe, Dataframe to Excel.
		</p>
	</div>

	<div>
	<h5>Session 5: Data Cleaning and preparation</h5>
	</div> <div></div>
	<div>	 
		<p> <strong>Data Cleaning and preparation</strong>  
		<br />Finding, replacing and filtering missing data.
		<br />Remove Duplicates.
		<br />Replacing values.
		<br />Renaming Axis Indexes.
		</p>
	</div>
	
	<div>
	<h5>Session 6: Pandas Data Wrangling</h5>
	</div> <div></div>
	<div>	 
		<p> <strong>Data Wrangling:</strong>   
		
		<br />Indexing, Re-indexing, 
		<br />Reorder, 
		<br />Sorting, Stastitics, 
			<br />Transforming data using  
		<br />Dataframe Joins, Merging, Concatenation, Overlap, Append, zip. 
		 
		</p>
	</div>
	
	<div>
	<h5>Week 6: Pandas Data Analysis</h5>
	</div> <div></div>
	<div>	 
		<p> <strong>Data Analysis:</strong>   
		<br /> Sorting. 
		<br /> Analysing and finding data using filter, slicing and dataframe queries. 
		<br /> Finding data by Iteration.
		<br /> Find statistics: Functions, Aggregate functions. 
		<br /> Unique values.
		<br />String objects, Regex. 
		</p>
	</div>
</div>
 

<div>   

	   <div id="cincluded">
		  <div>
			 <h5>Included with the Python Course</h5>
		  </div>

		  <article>
		  <div>
			 <ul>
				<li>PCWorkshops Course Certificate on completion  </li>
									   
				<li>Python Course Notes</li>
				<li>Python Code Examples</li>
				<li>Practical Python Course exercises and Code Samples</li>
				<li>After the course: 1-Hour personalised online revision session</li>
			 </ul>
		  </div>
		  </article>
	   </div>
				    
<div>
	                  <div>
	                     <h5 id="cincluded2">Book the Python Course</h5>
	                  </div>
 
	 
		<div id="eventbrite-widget-container-48025096356"></div>
		
		  		
					
					 
</div>
				   <div id="caboutus">
	                  <div>
	                     <h5>About us</h5>
	                  </div> 
	                  	<article>
					    <div>
					    	<div>
					    		<div>
					    			
					    			<h5>Our experienced trainers are award winners. </h5> 
					    			<ul>
										<li>
											More about us
										</li>
										<li>
											FAQ's
										</li>
										<li>
											Client Comments  
										</li>
									 </ul>
					    		</div>
					    		<div>
					    			
					    				
					    			
					    		</div>
					    	</div>
	                     
					    </div>
					    </article>						
	               </div>
				   
				   
	        </div>

Python Data Analytics Course

Python Data Analytics with Python using 
Numpy, 
Pandas, Dataframes. 

Most attendees are in-work Data Professional.  Private individuals are very welcome. 
Our Style: Hands-on, Practical 
Location: Online, Instructor-led 
Download: anaconda.com 
Duration: 1 consecutive day, 11am-5pm
Dates Times: You may let us know which dates your would prefer, or attend as scheduled

Python Data Analytics Course Description
  
Session 1: Database:
 SQLite3: 
 Linking with SQL Database. 
 Create, Amend and Drop Tables. 
 Insert, Update and delete records. 
 Select queries. 
 Traverse and display query results. 
 Using parameters in queries. 
 Get metadata about tables and columns. 
Session 2: Data Structures
 Data Structures: 
Tuples. 
Sets. 
Dictionary. Nested Dictionary. 
Json Files. 
Session 3: Python Numpy
 Numpy: 
The Python NumPy Module: 
Working with arrays, create data using arrays. 
Array manipulation. 
String functions on arrays. 
Math, arithmetic and statistical functions. 
Session 4: Python Pandas Dataframes and data importing
 Python Dataframes 
 Data Series. Date/ Time Functionality. Time series. 
 Creating Dataframes, Indexing. 
Dict to Dataframe, Dataframe to Dict. 
Csv to Dataframe, Dataframe to csv. 
Excel to Dataframe, Dataframe to Excel. 
Session 5: Data Cleaning and preparation
 Data Cleaning and preparation 
Finding, replacing and filtering missing data. 
Remove Duplicates. 
Replacing values. 
Renaming Axis Indexes. 
Session 6: Pandas Data Wrangling
 Data Wrangling: 
Indexing, Re-indexing, 
Reorder, 
Sorting, Stastitics, 
Transforming data using 
Dataframe Joins, Merging, Concatenation, Overlap, Append, zip. 
Week 6: Pandas Data Analysis
 Data Analysis: 
 Sorting. 
 Analysing and finding data using filter, slicing and dataframe queries. 
 Finding data by Iteration. 
 Find statistics: Functions, Aggregate functions. 
 Unique values. 
String objects, Regex. 
Included with the Python Course
PCWorkshops Course Certificate on completion
Python Course Notes
Python Code Examples
Practical Python Course exercises and Code Samples
After the course: 1-Hour personalised online revision session
 

Python Data Analytics with Python using 



Numpy, 




Pandas, Dataframes. 





Most attendees are in-work Data Professional.  Private individuals are very welcome. 
Our Style: Hands-on, Practical 
Location: Online, Instructor-led 



Python Data Analytics Course

<article>
<div>
<h5>Java Machine Learning Course</h5>
</div>
<div></div>
<p>  						 
	</p><div>
	<ul>
		<p>
		<b>Python Machine Learning Course. </b>
		<br /><b>Prerequisites: </b>Basic knowledge of Python 
		<br /><b>Our Style: </b>Hands-on, Practical  
		<br /><b>Location:  </b>Online, Instructor-led  
		<br /><b>Download:  </b>anaconda.com
		<br /><b>Duration:  </b>2 consecutive days, the first of which shows as the booking date.
		<br /><b>Times: </b>10am - 5pm
	    </p>
	</ul>	

<p></p>
</div></article>

<div>
	  <div>
		 <h5>Python Machine Learning Course </h5>
	  </div>
	  <div></div>
		<p> Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business.
		<br />
		<br />Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data.
		<br />
		<br />Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms.
		<br />
		<br />Gain a good understanding of how to plan a Machine Learning project.
		We create, experiment and run example code to implement the Python Machine Learning algorithms.
	  </p>
<div>
<h5>Course Topics</h5>
</div>
<div></div>
<div>
	<ul>	  
			<li>Supervised Machine Learning:
			<strong>Classification Algorithms:</strong>  	 
					Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine
					<br /><strong>Regression Algorithms:</strong>  Linear, Polynomial
			</li> 
	
	
			<li> <strong>  Unsupervised Machine Learning: </strong> Clustering Algorithms: K-means clustering, Hierarchical Clustering

			<br /><strong>Dimension Reduction Algorithms: </strong>Principal Component Analysis Latent Dirichlet allocation (LDA)

			<br /><strong>Association Algorithms:</strong> Apriori, Euclat

			<br /><strong>Other machine learning Algorithms:</strong>
			Reinforcement learning Algorithms: Q-Learning
			</li>
	
			<li>
			<strong>Ensemble Methods Algorithms:</strong> Stacking, bagging, boosting.
			</li> 
			
			<li>
			<strong>Random </strong>Forest Random Forest, Gradient Boosting
			</li> 
	 
		<li><strong>Neural Networks and Deep Leaning Algorithms:</strong>
		Convolutional Network (CNN)
		</li> 
		
		<li><strong>Data Exploration and Preprocessing:</strong> The first part of a Machine Learning project understands the data and the problem at hand. Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy. 
		</li>
	
		<li> Injecting domain knowledge in the process, attributes are extracted from the data and engineered into Machine Learning algorithms.
		</li> 
</ul>
</div>
		  
		
		 
</div>

Python Scikit-Learn Machine Learning Course


Prerequisites: Basic knowledge of Python 
Our Style: Hands-on, Practical 
Location: Online, Instructor-led 
Download: anaconda.com 
Duration: 1 days
Times: 11am - 5pm 
How to attend: 
You can select your dates and use the link  https://meet.goto.com/910697125  to attend
Python Machine Learning Course Description
 Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business. 
 
Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data. 
 
Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms. 
 
Gain a good understanding of how to plan a Machine Learning project. We create, experiment, and run example code to implement the Python Machine Learning algorithms. 
Course Topics
Supervised Machine Learning: Classification Algorithms: Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine 
Regression Algorithms: Linear, Polynomial
Unsupervised Machine Learning: Clustering Algorithms: K-means clustering, Hierarchical Clustering 
Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA) 
Association Algorithms: Apriori, Euclat 
Other machine learning Algorithms: Reinforcement learning Algorithms: Q-Learning
Ensemble Methods Algorithms: Stacking, bagging, boosting.
Random Forest Random Forest, Gradient Boosting
Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN)
Data Exploration and Preprocessing: The first part of a Machine Learning project understands the data and the problem at hand. Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy.
Injecting domain knowledge in the process, attributes are extracted from the data and engineered into Machine Learning algorithms.

Unsupervised Machine Learning

Python Machine Learning algorithms can derive trends (learn) from data and make predictions on data by extrapolating on existing trends. Companies can take advantage of this to gain insights and ultimately improve business. 
 
Using Python Machine Learning scikit-learn, practice how to use Python Machine Learning algorithms to perform predictions on data. 
 
Learn the below listed algorithms, a small collection of available Python Machine Learning algorithms. 

Python Machine Learning, online instructor-led 

<div style="color: rgb(57, 54, 79); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(57, 54, 79);"><div data-testid="summary" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><div data-testid="summary" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python coding, interactive, instructor-led, hands-on, practical. From basics to working with professionals. Python Part Time. Python Coding.</strong></p></div><div data-testid="organizerBrief" fr-original-style="" style="box-sizing: border-box;"><div data-testid="creator-signal-main-section" fr-original-style="" style="box-sizing: border-box;"><section fr-original-style="" style="box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></section></div></div></div><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Beginners Evenings Course</strong></strong></p><h2 style="color: rgb(30, 10, 60); box-sizing: border-box;" fr-original-style=" color: rgb(30, 10, 60);">Course summary</h2><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">This Python Programming course has become essential to all professional who would like to do code using Python. Learn evenings, work while studying. Work and study Python Part Time.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">It is a Python beginners course and teaches Python coding from scratch to intermediate.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Hands-on, practical. We code some real life examples in this course.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><em fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Duration:</strong></strong></em><em fr-original-style="" style="box-sizing: border-box;"><em fr-original-style="" style="box-sizing: border-box;">&nbsp;3 Evenings, once per week for 3 weeks 6pm-8pm, of which the first date only shows on the booking.</em></em></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Virtual Classroom:&nbsp;</strong></strong>You could attend in our classroom, or attend online via virtual classroom, by select the appropriate ticket.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Who Should Attend?</strong></strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">This course is intended for beginner programmers who need to code in Python. Fast-paced, Quite intensive.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course Outline:</strong></strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Session&nbsp;</strong></strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">1: Basics</strong></strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Data Types, Variables,</strong></strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Operators and Expressions:</strong></strong>&nbsp;Arithmetic operators; Relational operators; Assignment operator; Logical operators.</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Decision Making:</strong></strong>&nbsp;If statement; If - else statement; If- elif - else statement; Nested if - else;</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Using Loops:</strong></strong>&nbsp;The while, while-else; do-while and the for loop; Enhanced for loop; Jump statements: break, continue; Nesting loops.</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Session&nbsp;</strong></strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">2: Lists. Dictionaries, Json</strong></strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Python Data Structures: Lists. Data Structures: 2-d Lists, Tuples. Dictionary, Nested Disctionary.</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Session&nbsp;</strong></strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">3: Built-in functions:</strong></strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Python Date Functions. Random. Math Functions. String Functions. Python Regex.</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Session&nbsp;</strong></strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">4: Database:</strong></strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Connect to a database, create Database, create a table, insert, delete, update records, query a database and display results. Drop database, drop table.</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Session&nbsp;</strong></strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">5: Files</strong></strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Exception Handling in Python<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">:&nbsp;</strong></strong>Fundamentals of exception handling; Exception types; Using try and except.</li><li fr-original-style="" style="box-sizing: border-box;">Files, streams: Create<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">,&nbsp;</strong></strong>Open, Traverse and Read Files: Csv, txt and Json Files.</li><li fr-original-style="" style="box-sizing: border-box;">API: Connecting to API&rsquo;s</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Session&nbsp;</strong></strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">6</strong></strong>:&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">M ethods, Classes and Objects</strong></strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Creating and using Function, Working with parameters, Calling functions.</li><li fr-original-style="" style="box-sizing: border-box;">Object-Oriented Programming (OOP) principles. Modules and Classes, Creating an Object; Using an Object; Adding Instance variables; Controlling accessibility; Naming conventions for class member.s</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><br fr-original-style="" style="box-sizing: border-box;"></p><div data-testid="image-content" fr-original-style="" style="box-sizing: border-box;"><img src="https://img.evbuc.com/https%3A%2F%2Fcdn.evbuc.com%2Fimages%2F447713699%2F44980801875%2F1%2Foriginal.20230216-130657?h=2000&w=720&auto=format%2Ccompress&q=75&sharp=10&s=c331eac85ab8cd495a7a46a3aa8c2b7b" alt="" data-airgap-id="86" fr-original-style="" style="cursor: pointer; padding: 0px 1px; user-select: none; max-width: 100%; position: relative; box-sizing: border-box;" fr-original-class="fr-draggable"></div><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">FAQ</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Do I need Python on my Laptop?</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Ideally spoken yes, but if not possible, you could use our online platform.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Can I pay by BACS?</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Yes, simply email us, a Python course consultant will contact you. training@pcworkshopslondon.co.uk or pcworkshopslondon@gmail.com</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Can I pay with PayPal?</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Yes, simply email us, a course consultant will contact you. training@pcworkshopslondon.co.uk or pcworkshopslondon@gmail.com</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Do I get an invoice?</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Yes, on request.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Can I pay with a PO (Purchase Order Number)?</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Yes, simply email us, a Python course consultant will contact you. training@pcworkshopslondon.co.uk or pcworkshopslondon@gmail.com</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Do I get a certificate?</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Yes, a PCWorkshops Certificate</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Can I attend in classroom?</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Yes, on request, and it would incur an additional charge, depending on your location. We have offices in London, Manchester, Birmingham, Portmouth, Southend-on-Sea, Milton Keynes.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Can beginners follow this course?</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Yes, this is a Python beginner friendly course. We offer Python courses with max 4 people, so we make sure each delegates fully understand the material covered</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Can I customise the course?</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Yes, all our Python courses are customisable. We can customise the dates, times, location. course outline.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"></p></div><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></p><div style="color: rgb(30, 10, 60); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(30, 10, 60);"><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><h2 fr-original-style="" style="box-sizing: border-box;">Refund Policy</h2></div><div style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">No Refunds</div>

Python coding, interactive, instructor-led, hands-on, practical. From basics to working with professionals. Python Part Time. Python Coding.



Python Beginners Evenings Course
Course summary
This Python Programming course has become essential to all professional who would like to do code using Python. Learn evenings, work while studying. Work and study Python Part Time.
It is a Python beginners course and teaches Python coding from scratch to intermediate.
Hands-on, practical. We code some real life examples in this course.
Duration: 4 Evenings, once per week for 34weeks 6pm-8pm, of which the first date only shows on the booking.
Virtual Classroom: You could attend in our classroom, or attend online via virtual classroom, by select the appropriate ticket.
Who Should Attend?
This course is intended for beginner programmers who need to code in Python. Fast-paced, Quite intensive.
Course Outline:
Session 1: Basics
Python Data Types, Variables,
Operators and Expressions: Arithmetic operators; Relational operators; Assignment operator; Logical operators.
Decision Making: If statement; If - else statement; If- elif - else statement; Nested if - else;
Using Loops: The while, while-else; do-while and the for loop; Enhanced for loop; Jump statements: break, continue; Nesting loops.
Session 2: Lists. Dictionaries, Json
Python Data Structures: Lists. Data Structures: 2-d Lists, Tuples. Dictionary, Nested Disctionary. API's. Exceptions and CSV Files
Session 3: Built-in functions:
Python Random. Math Functions. String Functions.
Database:
Connect to a database, query a database and display results. 
Session 4: Methods, Classes and Objects
Creating and using Function, Working with parameters, Calling functions.
Object-Oriented Programming (OOP) principles. Modules and Classes, Creating an Object; Using an Object; Adding Instance variables; Controlling accessibility; Naming conventions for class member.s



FAQ
Do I need Python on my Laptop?
Ideally spoken yes, but if not possible, you could use our online platform.
Can I pay by BACS?
Yes, simply email us, a Python course consultant will contact you. training@pcworkshopslondon.co.uk or pcworkshopslondon@gmail.com
Can I pay with PayPal?
Yes, simply email us, a course consultant will contact you. training@pcworkshopslondon.co.uk or pcworkshopslondon@gmail.com
Do I get an invoice?
Yes, on request.
Can I pay with a PO (Purchase Order Number)?
Yes, simply email us, a Python course consultant will contact you. training@pcworkshopslondon.co.uk or pcworkshopslondon@gmail.com
Do I get a certificate?
Yes, a PCWorkshops Certificate
Can I attend in classroom?
Yes, on request, and it would incur an additional charge, depending on your location. We have offices in London, Manchester, Birmingham, Portmouth, Southend-on-Sea, Milton Keynes.
Can beginners follow this course?
Yes, this is a Python beginner friendly course. We offer Python courses with max 4 people, so we make sure each delegates fully understand the material covered
Can I customise the course?
Yes, all our Python courses are customisable. We can customise the dates, times, location. course outline.






Refund Policy
No Refunds

Course summary



This Python Programming course has become essential to all professional who would like to do code using Python. Learn evenings, work while studying. Work and study Python Part Time.



It is a Python beginners course and teaches Python coding from scratch to intermediate.



Hands-on, practical. We code some real life examples in this course.



Python Programming Beginners Evenings Course, Virtual Classroom.

Keep 020 7164 6359 for your contact on the day. Thanks for booking!

Python Evenings in Classroom, London

<div style="color: rgb(57, 54, 79); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(57, 54, 79);"><div data-testid="summary" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms.</strong></p></div><div data-testid="organizerBrief" fr-original-style="" style="box-sizing: border-box;"><div data-testid="creator-signal-main-section" fr-original-style="" style="box-sizing: border-box;"><section fr-original-style="" style="box-sizing: border-box;"></section></div></div><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></strong></p><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Machine Learning 2-day Course</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><em fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Prerequisites:</strong></em></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Basic knowledge of Python coding is a pre-requisite.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><em fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Who Should Attend?</strong></em></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Practical:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project</li><li fr-original-style="" style="box-sizing: border-box;">We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms.&nbsp;</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course Outline:</strong></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Supervised Machine Learning:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine</li><li fr-original-style="" style="box-sizing: border-box;">Regression Algorithms: Linear, Polynomial</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Unsupervised Machine Learning:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Clustering Algorithms: K-means clustering, Hierarchical Clustering</li><li fr-original-style="" style="box-sizing: border-box;">Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA)</li><li fr-original-style="" style="box-sizing: border-box;">Association Machine Learning Algorithms: Apriori, Euclat</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Other machine learning Algorithms:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting</li><li fr-original-style="" style="box-sizing: border-box;">Reinforcement learning Algorithms: Q-Learning</li><li fr-original-style="" style="box-sizing: border-box;">Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN)</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Exploration and Preprocessing:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">The first part of a Machine Learning project understands the data and the problem at hand.</li><li fr-original-style="" style="box-sizing: border-box;">Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy.</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"></p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What is included in this Python Machine Learning:</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Python Machine Learning Certificate on completion &nbsp;</li><li fr-original-style="" style="box-sizing: border-box;">Python Machine Learning notes</li><li fr-original-style="" style="box-sizing: border-box;">Practical Python Machine Learning exercises and code examples</li><li fr-original-style="" style="box-sizing: border-box;">After the course, 1 free, online session for questions or revision Python Machine Learning.</li><li fr-original-style="" style="box-sizing: border-box;">Max group size on this Python Machine Learning is 4.</li></ul></div><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></p><div style="color: rgb(30, 10, 60); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(30, 10, 60);"><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><h2 fr-original-style="" style="box-sizing: border-box;">Refund Policy</h2></div><div style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">No Refunds</div>

This Python Machine Learning online instructor led course is an excellent introduction to popular machine learning algorithms.





Python Machine Learning 2-day Course
Prerequisites:
Basic knowledge of Python coding is a pre-requisite.
Who Should Attend?
This course is an overview of machine learning and machine learning algorithms in Python SciKitLearn.
Practical:
We cover the below listed algorithms, which is only a small collection of what is available. However, it will give you a good understanding, to plan your Machine Learning project
We create, experiment and run machine learning sample code to implement a short selected but representative list of available the algorithms.
Course Outline:
Supervised Machine Learning:
Classification Algorithms: Naive Bayes, Decision Tree, Logistic Regression, K-Nearest Neighbors, Support Vector Machine
Regression Algorithms: Linear, Polynomial
Unsupervised Machine Learning:
Clustering Algorithms: K-means clustering, Hierarchical Clustering
Dimension Reduction Algorithms: Principal Component Analysis Latent Dirichlet allocation (LDA)
Association Machine Learning Algorithms: Apriori, Euclat
Other machine learning Algorithms:
Ensemble Methods ( Stacking, bagging, boosting ) Algorithms: Random Forest, Gradient Boosting
Reinforcement learning Algorithms: Q-Learning
Neural Networks and Deep Leaning Algorithms: Convolutional Network (CNN)
Data Exploration and Preprocessing:
The first part of a Machine Learning project understands the data and the problem at hand.
Data cleaning, data transformation and data pre-processing are covered using Python functions to make data exploration and preprocessing relatively easy.


What is included in this Python Machine Learning:
Python Machine Learning Certificate on completion
Python Machine Learning notes
Practical Python Machine Learning exercises and code examples
After the course, 1 free, online session for questions or revision Python Machine Learning.
Max group size on this Python Machine Learning is 4.





Refund Policy
No Refunds

Python Machine Learning Course, 1-Days, Online Attendance

Python Basics 1-days Online Instructor-Led

<article>
<div>
<h5>Java Course 1 hour. Code the Hangman</h5>
</div>
<div></div>
<p>  						 
	</p><div>
	<ul>
		<p>
		<b>Python Course, code the Hangman. </b>Basic Python Course covers in one hour the coding of an interesting app. 
		<br />Any one very welcome.  
		<br /><b>Our Style: </b>Hands-on, Practical  
		<br /><b>Location:  </b>Online, Instructor-led  
		<br /><b>Download:  </b>anaconda.com
		<br /><b>Duration:  </b>1 Hour
		<br /><b>Times: </b>arrange a time for your time zone
	    </p>
	</ul>	

<p></p>
</div></article>

<div>
	  <div>
		 <h5>Python Basics Course Description</h5>
	  </div>
	  <div></div>
	  <p> Learn to code the Hangman Game in 1 hour. 
		<br /> Essentials of Python programming, explaining Python technology. 
		<br /> Practical Python lessons and Python exercises. 
		<br /> Great Tutors. 
		<br /> Online. 
		<br /> Learn plenty in one day: Code The Hangman Game in Python
	  </p>
	<div>
	<h5>Code the Hangman Game</h5>
	</div>
	<div></div>
	<div> 
		<p>  <strong>Python Course 1 Hour:</strong>  
		<br />  	STEP 1: Using variables. Arithmetic operations. Strings. String Functions
		<br />  	STEP 2: Getting user input from console. Decision Making (If-else).
		<br />  	STEP 3: For and While Loops. Repeat code by using loops.
		<br />  	STEP 4: Lists: Create, Update, Iterate Lists. Using indexes to refer to individual values.
		<br />  	STEP 5: Object Oriented Programming: Functions. Classes.  
		<br />  	STEP 6: Create the Hangman Game.			
		</p>
	</div>
 
</div>
 

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		  <div>
			 <h5>What is included with The Python Course 1 Hour</h5>
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		  <article>
		  <div>
			 <ul>
				<li>PCWorkshops Python Course Certificate on completion  </li> 
				<li>Python Course Notes</li>
				<li>Python Code Examples</li>
				<li>Practical Python Course exercises and Code Samples</li>
				<li>After the course: 1-Hour personalised online revision session</li>
			 </ul>
		  </div>
		  </article>
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	                     <h5 id="cincluded2">Book the Python Course 1 hour</h5>
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				   <div id="caboutus">
	                  <div>
	                     <h5>About us</h5>
	                  </div> 
	                  	<article>
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					    			<h5>Our experienced trainers are award winners. </h5> 
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											More about us
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										</li>
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	        </div>

Java Course 1 hour. Code the Hangman
Online, Instructor-led
 
 Python Course, code the Hangman. Basic Python Course covers in one hour the coding of an interesting app. 
Any one very welcome. 
Our Style: Hands-on, Practical 

Location: Online, Instructor-led 
Download: anaconda.com 
Duration: 1 Hour 
Times: arrange a time for your time zone 
Python Basics Course Description
 Learn to code the Hangman Game in 1 hour. 
 Essentials of Python programming, explaining Python technology. 
 Practical Python lessons and Python exercises. 
 Great Tutors. 
 Online. 
 Learn plenty in one day: Code The Hangman Game in Python 
Code the Hangman Game
 Python Course 1 Hour: 
 STEP 1: Using variables. Arithmetic operations. Strings. String Functions 
 STEP 2: Getting user input from console. Decision Making (If-else). 
 STEP 3: For and While Loops. Repeat code by using loops. 
 STEP 4: Lists: Create, Update, Iterate Lists. Using indexes to refer to individual values. 
 STEP 5: Object Oriented Programming: Functions. Classes. 
 STEP 6: Create the Hangman Game. 
What is included with The Python Course 1 Hour
PCWorkshops Python Course Certificate on completion
Python Course Notes
Python Code Examples
Practical Python Course exercises and Code Samples
After the course: 1-Hour personalised online revision session
Book the Python Course 1 hour
About us
Our experienced trainers are award winners.
More about us
FAQ's
Client Comments

Application Software

Computing & Software Development Tools

Coding For Kids

Remote Desktop Software

Software Development

Python Course, code the Hangman. Basic Python Course covers in one hour the coding of an interesting app. 
Any one very welcome. 
Our Style: Hands-on, Practical 
Location: Online, Instructor-led 
Download: anaconda.com 
Duration: 1 Hour 
Times: arrange a time for your time zone 

Python Taster, Code the Hangman, Online Instrcutor-led 

<p><strong> Boost Your Career By Enrolling On This Python Course To Get Through Challenging Moments!</strong></p><p><em><strong>Become a Python programmer and study one of the most in-demand talents for employers in the upcoming year!</strong></em></p><p>This is the most extensive, but simple, Python programming language course available! This programming language course is for you whether you've never coded before, know basic grammar, or want to learn about Python's advanced techniques. You will learn the Python programming language course the way that suits you best!</p><p>This course is accredited by <strong>CPD </strong>with <strong>10 CPD points</strong> for professional development. Students can expect to complete this course in around <strong>09 hours</strong>. You'll also get dedicated expert assistance from us to answer any queries you may have while studying our course.</p><p>Upon successful completion of the Python course, an instant e-certificate will be exhibited in your profile that you can order as proof of your new skills and knowledge. Add these amazing new skills to your resume and boost your employability by simply enrolling in this course.</p><p>You'll get a <strong>Free Student ID</strong> card by enrolling in this course. This ID card entitles you to discounts on bus tickets, movie tickets, and library cards.</p><p>This course can help you to accomplish your ambitions and prepare you for a meaningful career. So, join the course today - gear up with the essential skills to set yourself up for excellence!</p><p><strong>What you will learn from this Course?</strong></p><ul><li>Become a skilled Developer</li><li>How to use the basic structures: strings, lists, and dictionaries</li><li>How to use Object-Oriented Programming (OOP)</li><li>How to use core programming tools such as functions and loops</li><li>How to land your first job as a Developer</li><li>write Python scripts to perform automated actions</li><li>Start creating your Python programs from scratch</li><li>Learn to use variables to store, retrieve and calculate information</li></ul><p><strong>Why should you choose this course with Academy for Health &amp; Fitness?</strong></p><ul><li>Opportunity to earn a certificate accredited by <strong>CPD </strong>after completing this course</li><li><strong>Student ID</strong> card with amazing discounts - completely for <strong> FREE!</strong> (£10 postal charges will be applicable for international delivery)</li><li>Globally accepted standard structured lesson planning</li><li>Innovative and engaging content and activities</li><li>Assessments that measure higher-level thinking and skills</li><li>Complete the program in your own time, at your own pace</li><li>Each of our students gets full 24/7 tutor support</li></ul> <p>With this comprehensive course, you can achieve your dreams and train for your ideal career. The Python course provides students with an in-depth learning experience that they can work through at their own pace to enhance their professional development.</p><p>Start your learning journey straight away with this course and take a step toward a brighter future!</p><p><strong>*** Course Curriculum ***</strong></p><p><strong></strong><strong> Learn Programming with Python Course</strong><strong></strong></p><p><strong>Module 01:</strong></p><ul><li>An Installing Python</li><li>Documentation</li><li>Command-Line</li><li>Variables</li><li>Simple Python Syntax</li><li>Keywords</li><li>Import-Module</li></ul><p><strong>Module 02:</strong></p><ul><li>Additional Topics</li><li>If Elif Else</li><li>Iterable</li><li>For</li><li>Loops in Python</li><li>Execute</li><li>Exceptions</li></ul><p><strong>Module 03:</strong></p><ul><li>Data Types</li><li>Number Types</li><li>More Number Types</li><li>Strings</li><li>More Strings</li><li>Files</li><li>Lists</li><li>Dictionaries</li><li>Tuples</li><li>Sets</li></ul><p><strong>Module 04:</strong></p><ul><li>Comprehensions</li><li>Definitions</li><li>Functions</li><li>Default Arguments</li><li>Doc Strings</li><li>Variadic Functions</li><li>Factorial</li></ul><p><strong>Module 05:</strong></p><ul><li>Function Objects</li><li>Lambda</li><li>Generators</li><li>Object Initialization</li><li>Class Static Members</li><li>Classic Inheritance</li><li>Data Hiding</li></ul><p><strong>Assessment Process</strong></p><p>Once you have completed all the modules in the Python course, your skills and knowledge will be tested with an automated multiple-choice assessment. You will then receive instant results to let you know if you have successfully passed the course.</p><p><strong>Show off Your New Skills with a Certificate of Completion</strong></p><p>The learners have to successfully complete the assessment of this Course to achieve the <strong>CPD accredited certificate.</strong> Digital certificates can be ordered for only £10. The learner can purchase printed hard copies inside the UK for £29, and international students can purchase printed hard copies for £39.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        10 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div> <h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>Anyone interested in learning more about the topic is advised to take this course. This course is open to everybody.</p>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You will not need any prior background or expertise to enrol in this course.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>After completing this course, you are to start your career or begin the next phase of your career.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £10
        </p>
        <h4>CPD Accredited Certificate </h4>
        <p>
            Hard copy certificate -
            £29
        </p>
            <div> <p>If you are an international student, then you have to pay an additional 10 GBP as an international delivery charge.</p> </div>

                        </div>

Boost Your Career By Enrolling On This Python Course To Get Through Challenging Moments!
Become a Python programmer and study one of the most in-demand talents for employers in the upcoming year!
This is the most extensive, but simple, Python programming language course available! This programming language course is for you whether you've never coded before, know basic grammar, or want to learn about Python's advanced techniques. You will learn the Python programming language course the way that suits you best!
This course is accredited by CPD with 10 CPD points for professional development. Students can expect to complete this course in around 09 hours. You'll also get dedicated expert assistance from us to answer any queries you may have while studying our course.
Upon successful completion of the Python course, an instant e-certificate will be exhibited in your profile that you can order as proof of your new skills and knowledge. Add these amazing new skills to your resume and boost your employability by simply enrolling in this course.
You'll get a Free Student ID card by enrolling in this course. This ID card entitles you to discounts on bus tickets, movie tickets, and library cards.
This course can help you to accomplish your ambitions and prepare you for a meaningful career. So, join the course today - gear up with the essential skills to set yourself up for excellence!
What you will learn from this Python Course?
Become a skilled Developer
How to use the basic structures: strings, lists, and dictionaries
How to use Object-Oriented Programming (OOP)
How to use core programming tools such as functions and loops
How to land your first job as a Developer
write Python scripts to perform automated actions
Start creating your Python programs from scratch
Learn to use variables to store, retrieve and calculate information
Why should you choose this course with Academy for Health & Fitness?
Opportunity to earn a certificate accredited by CPD after completing this course
Student ID card with amazing discounts - completely for FREE! (£10 postal charges will be applicable for international delivery)
Globally accepted standard structured lesson planning
Innovative and engaging content and activities
Assessments that measure higher-level thinking and skills
Complete the program in your own time, at your own pace
Each of our students gets full 24/7 tutor support
With this comprehensive course, you can achieve your dreams and train for your ideal career. The Python course provides students with an in-depth learning experience that they can work through at their own pace to enhance their professional development.



Start your learning journey straight away with this Python course and take a step toward a brighter future!
*** Course Curriculum ***
Learn Programming with Python Course
Module 01:
An Installing Python
Documentation
Command-Line
Variables
Simple Python Syntax
Keywords
Import-Module
Module 02:
Additional Topics
If Elif Else
Iterable
For
Loops in Python
Execute
Exceptions
Module 03:
Data Types
Number Types
More Number Types
Strings
More Strings
Files
Lists
Dictionaries
Tuples
Sets
Module 04:
Comprehensions
Definitions
Functions
Default Arguments
Doc Strings
Variadic Functions
Factorial
Module 05:
Function Objects
Lambda
Generators
Object Initialization
Class Static Members
Classic Inheritance
Data Hiding
Assessment Process
Once you have completed all the modules in the Python course, your skills and knowledge will be tested with an automated multiple-choice assessment. You will then receive instant results to let you know if you have successfully passed the course.
Show off Your New Skills with a Certificate of Completion
The learners have to successfully complete the assessment of this Course to achieve the CPD accredited certificate. Digital certificates can be ordered for only £10. The learner can purchase printed hard copies inside the UK for £29, and international students can purchase printed hard copies for £39.
CPD 
10 CPD hours / points  Accredited by CPD Quality Standards



Who is this course for? 
Anyone interested in learning more about the topic is advised to take this Python course. This course is open to everybody.



Requirements 
You will not need any prior background or expertise to enrol in this Python course.



Career path
After completing this Python course, you are to start your career or begin the next phase of your career.



Certificates 
Certificate of completion
Digital certificate - £10 
CPD Accredited Certificate
Hard copy certificate - £29 
If you are an international student, then you have to pay an additional 10 GBP as an international delivery charge.

Academy for Health and Fitness

Assessment Practising Certificate (APC)

Python - CPD Accredited

<div style="color: rgb(57, 54, 79); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(57, 54, 79);"><div data-testid="summary" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><div data-testid="summary" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Powerful 1-hour Python workshop course understand Python Basics. Practical. Instructor-led. Online.</strong></p></div><div data-testid="organizerBrief" fr-original-style="" style="box-sizing: border-box;"><div data-testid="creator-signal-main-section" fr-original-style="" style="box-sizing: border-box;"><section fr-original-style="" style="box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></section></div></div></div><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><h1 style="color: rgb(57, 54, 79); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(57, 54, 79);">Learn to code a Password Validator in 1 hour.&nbsp;</h1><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">Basics made simple! &nbsp;Learn plenty in one day: Code A Password Validator</p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Essentials of Python programming.</li><li fr-original-style="" style="box-sizing: border-box;">Practical lessons and exercises.</li><li fr-original-style="" style="box-sizing: border-box;">Great Tutors.&nbsp;</li><li fr-original-style="" style="box-sizing: border-box;">Online.</li><li fr-original-style="" style="box-sizing: border-box;">You may Request a Central London Location, per email</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Who will the Course Benefit?</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">This is a fantastic course for beginners</li><li fr-original-style="" style="box-sizing: border-box;">Or for coders, simply fast-track crossing over to Python.</li></ul><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Teaching Style:&nbsp;</strong>Practical, Interactive, Personalised. Useful examples.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What is included?</strong></p><ul style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><li fr-original-style="" style="box-sizing: border-box;">Course notes, exercises and examples.</li><li fr-original-style="" style="box-sizing: border-box;">Course certificate.</li></ul><h2 style="color: rgb(30, 10, 60); box-sizing: border-box;" fr-original-style=" color: rgb(30, 10, 60);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learn Python by coding a Password Validator</strong></h2><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">STEP 1:&nbsp;</strong>Using variables. Arithmetic operations. Strings. String Functions</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">STEP 2:&nbsp;</strong>Getting user input from console. Decision Making(If-else)</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">STEP 3:&nbsp;</strong>For and While Loops. Repeat code by using loops.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">STEP 4:&nbsp;</strong>Lists: Create, Update, Iterate Lists. Using indexes to refer to individual values.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">STEP 5:&nbsp;</strong>Object Oriented Programming. Functions. Classes.</p><p style="color: rgb(111, 114, 135); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">STEP 6:&nbsp;</strong>Create the Password Validator</p></div><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></p><div style="color: rgb(30, 10, 60); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(30, 10, 60);"><hr fr-original-style="" style="border-color: var(--border-muted); border-style: solid; clear: both; break-after: page; user-select: none; box-sizing: border-box;"><h2 fr-original-style="" style="box-sizing: border-box;">Refund Policy</h2></div><div style="color: rgb(111, 114, 135); box-sizing: border-box;" fr-original-style=" color: rgb(111, 114, 135);">No Refunds</div>

Powerful 1-hour Python workshop course understand Python Basics. Practical. Instructor-led. Online.



Learn to code a Password Validator in 1 hour. 
Basics made simple! Learn plenty in one day: Code A Password Validator
Essentials of Python programming.
Practical lessons and exercises.
Great Tutors.
Online.
You may Request a Central London Location, per email
Who will the Course Benefit?
This is a fantastic course for beginners
Or for coders, simply fast-track crossing over to Python.
Teaching Style: Practical, Interactive, Personalised. Useful examples.
What is included?
Course notes, exercises and examples.
Course certificate.
Learn Python by coding a Password Validator
STEP 1: Using variables. Arithmetic operations. Strings. String Functions
STEP 2: Getting user input from console. Decision Making(If-else)
STEP 3: For and While Loops. Repeat code by using loops.
STEP 4: Lists: Create, Update, Iterate Lists. Using indexes to refer to individual values.
STEP 5: Object Oriented Programming. Functions. Classes.
STEP 6: Create the Password Validator





Refund Policy
No Refunds

Powerful 1-hour Python workshop course understand Python Basics. Practical. Instructor-led. Online.
Learn to code a Password Validator in 1 hour. 



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