Name: COVID-19 Awareness: Part 2
Price: 25 GBP
Availability: InStock

Packt

Data Science

Data Visualization

Data Analysis

Python

Pandas

Welcome to the data manipulation in Python course. Our goal in this course is to provide you with all the tools and skills   necessary to master Python, NumPy, and Pandas for data science. No previous skills or expertise are required. Only a drive to succeed!

Data Manipulation in Python - Master Python, NumPy, and Pandas

General Admission

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				<p><span>Register on the Covid 19 Health Awareness and Food Safety today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career.</span></p><p><span>The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials.</span></p><p><span>Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion.</span></p><p><span>The Covid 19 Health Awareness and Food Safety course is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones.</span></p><p><span>The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly!</span></p>					</div>
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			<h2>What You Get With The Covid 19 Health Awareness and Food Safety course
</h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Receive a e-certificate upon successful completion of the course</span>
									</li>
								<li>
											<span>
													</span>
										<span>Get taught by experienced, professional instructors</span>
									</li>
								<li>
											<span>
													</span>
										<span>Study at a time and pace that suits your learning style</span>
									</li>
								<li>
											<span>
													</span>
										<span>Get instant feedback on assessments </span>
									</li>
								<li>
											<span>
													</span>
										<span>24/7 help and advice via email or live chat</span>
									</li>
								<li>
											<span>
													</span>
										<span>Get full tutor support on weekdays (Monday to Friday)</span>
									</li>
						</ul>
				</div>
				</div>
				<div>
				<div>
			<h2>Course Design</h2>		</div>
				</div>
				<div>
				<div>
								<div>
				<p><span>The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.</span></p><p><span>You are taught through a combination of</span></p>					</div>
						</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Video lessons</span>
									</li>
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											<span>
													</span>
										<span>Online study materials</span>
									</li>
								<li>
											<span>
													</span>
										<span>Mock exams</span>
									</li>
								<li>
											<span>
													</span>
										<span>Multiple-choice assessment</span>
									</li>
						</ul>
				</div>
				</div>
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			<h2>Assessment
</h2>		</div>
				</div>
				<div>
				<div>
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				<div id="course-home"><div><p><span>To successfully complete the Covid 19 Health Awareness and Food Safety course, students will have to take an automated multiple-choice exam. This exam will be online and you will need to score 60% or above to pass the course. After successfully passing the exam, you will be able to apply for a CPD-accredited certificate as proof of your subject-knowledge.</span></p></div></div>					</div>
						</div>
				</div>
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			<h2>Certification
</h2>		</div>
				</div>
				<div>
				<div>
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				<div id="course-home"><div><p><span>After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post.</span></p></div></div>					</div>
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			<h2>Who is this course for?</h2>		</div>
				</div>
				<div>
				<div>
								<div>
				<div id="course-home"><div><p><span>The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.</span></p></div></div>					</div>
						</div>
				</div>
				<div>
				<div>
			<h2>Requirements:
</h2>		</div>
				</div>
				<div>
				<div>
								<div>
				<p><span>The online training is open to all students and has no formal entry requirements. To study the Covid 19 Health Awareness and Food Safety course, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16.</span></p>					</div>
						</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>
		</section>
									</div>
			</div>
					</div><div>
				<h3>Course Content</h3>

<div id="course_curriculum">
<table width="100%">				<tbody><tr>
					<td colspan="4">Module 01: Introduction to Covid-19</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Covid-19</td>
					<td> </td>
					<td><span><i></i> 00:28:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 02: Understanding of Covid-19 as a Pandemic and How It Is Related to Zoonotic Diseases Like SARS and MERS </td>
				</tr>
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					<td><i></i></td>
					<td>Understanding of Covid-19 as a Pandemic and How It Is Related to Zoonotic Diseases Like SARS and MERS</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 03: Effects of Covid-19 on Human Life</td>
				</tr>
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					<td><i></i></td>
					<td>Effects of Covid-19 on Human Life</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
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					<td colspan="4">Module 04: Diagnoses and Treatments</td>
				</tr>
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					<td><i></i></td>
					<td>Diagnoses and Treatments</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
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					<td colspan="4">Module 05: Preventions and Social Measures to Be Taken</td>
				</tr>
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					<td><i></i></td>
					<td>Preventions and Social Measures to Be Taken</td>
					<td> </td>
					<td><span><i></i> 00:28:00</span></td>
				</tr>
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					<td colspan="4">Module 06: Artificial Intelligence, Data Science and Technological Solutions against Covid-19 </td>
				</tr>
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					<td><i></i></td>
					<td>Artificial Intelligence, Data Science and Technological Solutions against Covid-19</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
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					<td colspan="4">Module 07: Food Storage </td>
				</tr>
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					<td><i></i></td>
					<td>Food Storage</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
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					<td colspan="4">Module 08: Food Preparation and Processing for Food Manufacturing </td>
				</tr>
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					<td><i></i></td>
					<td>Food Preparation and Processing for Food Manufacturing</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 09: Food and Temperature Control</td>
				</tr>
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					<td><i></i></td>
					<td>Food and Temperature Control</td>
					<td> </td>
					<td><span><i></i> 00:31:00</span></td>
				</tr>
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					<td colspan="4">Module 10: Hazard Control Measures and Refrigeration </td>
				</tr>
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					<td><i></i></td>
					<td>Hazard Control Measures and Refrigeration</td>
					<td> </td>
					<td><span><i></i> 00:29:00</span></td>
				</tr>
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					<td colspan="4">Module 11: Physical, Chemical and Allergenic Hazards </td>
				</tr>
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					<td><i></i></td>
					<td>Physical, Chemical and Allergenic Hazards</td>
					<td> </td>
					<td><span><i></i> 00:27:00</span></td>
				</tr>
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					<td colspan="4">Module 12: Food Hygiene and Safety: Legislation </td>
				</tr>
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					<td><i></i></td>
					<td>Food Hygiene and Safety: Legislation</td>
					<td> </td>
					<td><span><i></i> 00:27:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Mock Exam</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Mock Exam- Covid 19 Health Awareness and Food Safety</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Final Exam </td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Final Exam- Covid 19 Health Awareness and Food Safety</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
			<h2>Frequently Asked Questions</h2>
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                                <span>
                                    Are there any prerequisites for taking the course?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        There are no specific prerequisites for this course, nor
                                        are there any formal entry requirements. All you need is
                                        an internet connection, a good understanding of English
                                        and a passion for learning for this course.
                                    </p>
                                </div>
                            </div>
                        </div>
                        
                        <div>
                            <div>
                                <span>
                                    Can I access the course at any time, or is there a set
                                    schedule?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        You have the flexibility to access the course at any time
                                        that suits your schedule. Our courses are self-paced,
                                        allowing you to study at your own pace and convenience.
                                    </p>
                                </div>
                            </div>
                        </div>
                        
                        <div>
                            <div>
                                <span>
                                    How long will I have access to the course?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        For this course, you will have access to the course
                                        materials for 1 year only. This means you can review the
                                        content as often as you like within the year, even after
                                        you've completed the course. However, if you buy Lifetime
                                        Access for the course, you will be able to access the
                                        course for a lifetime.
                                    </p>
                                </div>
                            </div>
                        </div>
                        
                        <div>
                            <div>
                                <span>
                                    Is there a certificate of completion provided after
                                    completing the course?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        Yes, upon successfully completing the course, you will
                                        receive a certificate of completion. This certificate can
                                        be a valuable addition to your professional portfolio and
                                        can be shared on your various social networks.
                                    </p>
                                </div>
                            </div>
                        </div>
                        
                        <div>
                            <div>
                                <span>
                                    Can I switch courses or get a refund if I'm not satisfied
                                    with the course?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        We want you to have a positive learning experience. If
                                        you're not satisfied with the course, you can request a
                                        course transfer or refund within 14 days of the initial
                                        purchase.
                                    </p>
                                </div>
                            </div>
                        </div>
                        
                        <div>
                            <div>
                                <span>
                                    How do I track my progress in the course?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        Our platform provides tracking tools and progress
                                        indicators for each course. You can monitor your progress,
                                        completed lessons, and assessments through your learner
                                        dashboard for the course.
                                    </p>
                                </div>
                            </div>
                        </div>
                        
                        <div>
                            <div>
                                <span>
                                    What if I have technical issues or difficulties with the
                                    course?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        If you encounter technical issues or content-related
                                        difficulties with the course, our support team is
                                        available to assist you. You can reach out to them for
                                        prompt resolution.
                                    </p>
                                </div>
                            </div>
                        </div>

Janets

Online Course Marketing

Online Course Creation

Certificate in Further Education & Training (Cert Ed)

Professional Skills Course (PSC)

Food & Hygiene

Trainers Course

Covid 19 Health Awareness and Food Safety

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							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
								<div>
				<p><span>Register on the Healthcare Assistant today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career.</span></p>
<p><span>The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials.</span></p>
<p><span>Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get an e-certificate as proof of your course completion.</span></p>
<p><span>The Healthcare Assistant is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones.</span></p>
<p><span>The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly!</span></p>
<h5><b>What You Get With The Healthcare Assistant</b></h5>
<ul>
<li><span>Receive a e-certificate upon successful completion of the course</span></li>
<li><span>Get taught by experienced, professional instructors</span></li>
<li><span>Study at a time and pace that suits your learning style</span></li>
<li><span>Get instant feedback on assessments </span></li>
<li><span>24/7 help and advice via email or live chat</span></li>
<li><span>Get full tutor support on weekdays (Monday to Friday)</span></li>
</ul>					</div>
						</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>
		</section>
				<section>
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					<div>
			<div>
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						<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>
		</section>
				<section>
						<div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
			<h2>Course Design</h2>		</div>
				</div>
				<div>
				<div>
								<div>
				<div id="course-home"><div><p><span>The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.</span></p><p><span>You are taught through a combination of</span></p><ul><li><span>Video lessons</span></li><li><span>Online study materials</span></li></ul></div></div>					</div>
						</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>
		</section>
				<section>
						<div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
			<h2>Certification</h2>		</div>
				</div>
				<div>
				<div>
								<div>
				<p><span>Upon successful completion of the course, you will be able to obtain your course completion e-certificate free of cost. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99.</span></p>					</div>
						</div>
				</div>
				<div>
				<div>
			<h2>Who Is This Course For:</h2>		</div>
				</div>
				<div>
				<div>
								<div>
				<p><span>The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.</span></p>					</div>
						</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>
		</section>
				<section>
						<div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
			<h2>Requirements:</h2>		</div>
				</div>
				<div>
				<div>
								<div>
				<p><span>The online training is open to all students and has no formal entry requirements. To study the Healthcare Assistant, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. </span></p>					</div>
						</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>
		</section>
									</div>
			</div>
					</div><div>
				<h3>Course Content</h3>

<div id="course_curriculum">
<table width="100%">				<tbody><tr>
					<td colspan="4">Module 01: Working in Different Healthcare Settings</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Working in Different Healthcare Settings</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 02: Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care Part - 1</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care Part - 1</td>
					<td> </td>
					<td><span><i></i> 00:50:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 03: Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care Part - 2</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Understanding Legal, Professional Standards of Practice and Ethical Aspects of Health Care Part - 2</td>
					<td> </td>
					<td><span><i></i> 00:48:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 04: Maintaining Medical Records</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Maintaining Medical Records</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 05: Confidentiality in a Medical Environment</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Confidentiality in a Medical Environment</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 06: Health and Safety Responsibilities</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Health and Safety Responsibilities</td>
					<td> </td>
					<td><span><i></i> 00:53:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 07: Hygiene in Nursing</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Hygiene in Nursing</td>
					<td> </td>
					<td><span><i></i> 00:28:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 08: Infection Control</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Infection Control</td>
					<td> </td>
					<td><span><i></i> 00:20:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 09: Mobility and Immobility Issues of Patients in Nursing</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Mobility and Immobility Issues of Patients in Nursing</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 10: Rights and Responsibilities as a Health and Social Care Worker</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Rights and Responsibilities as a Health and Social Care Worker</td>
					<td> </td>
					<td><span><i></i> 00:40:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 11: Role as A Caregiver and Healthcare Professional</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Role as a Caregiver and Healthcare Professional</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 12: Providing Care or Treatment to People Who Lack Capacity</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Providing Care or Treatment to People Who Lack Capacity</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 13: Managing Service Delivery in Health and Social Care</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Managing Service Delivery in Health and Social Care</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 14: Medical Jargon and Terminology</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Medical Jargon and Terminology</td>
					<td> </td>
					<td><span><i></i> 00:26:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 15: Effects of Covid-19 on Human Life</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Effects of Covid-19 on Human Life</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 16: Preventions and Social Measures to Be Taken</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Preventions and Social Measures to Be Taken</td>
					<td> </td>
					<td><span><i></i> 00:29:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 17: Information Technology in Health Care</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Information Technology in Health Care</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 18: Artificial Intelligence, Data Science and Technological Solutions against Covid-19</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Artificial Intelligence, Data Science and Technological Solutions against Covid-19</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
			<h2>Frequently Asked Questions</h2>
                        <div>
                            <div>
                                <span>
                                    Are there any prerequisites for taking the course?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        There are no specific prerequisites for this course, nor
                                        are there any formal entry requirements. All you need is
                                        an internet connection, a good understanding of English
                                        and a passion for learning for this course.
                                    </p>
                                </div>
                            </div>
                        </div>
                        
                        <div>
                            <div>
                                <span>
                                    Can I access the course at any time, or is there a set
                                    schedule?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        You have the flexibility to access the course at any time
                                        that suits your schedule. Our courses are self-paced,
                                        allowing you to study at your own pace and convenience.
                                    </p>
                                </div>
                            </div>
                        </div>
                        
                        <div>
                            <div>
                                <span>
                                    How long will I have access to the course?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        For this course, you will have access to the course
                                        materials for 1 year only. This means you can review the
                                        content as often as you like within the year, even after
                                        you've completed the course. However, if you buy Lifetime
                                        Access for the course, you will be able to access the
                                        course for a lifetime.
                                    </p>
                                </div>
                            </div>
                        </div>
                        
                        <div>
                            <div>
                                <span>
                                    Is there a certificate of completion provided after
                                    completing the course?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        Yes, upon successfully completing the course, you will
                                        receive a certificate of completion. This certificate can
                                        be a valuable addition to your professional portfolio and
                                        can be shared on your various social networks.
                                    </p>
                                </div>
                            </div>
                        </div>
                        
                        <div>
                            <div>
                                <span>
                                    Can I switch courses or get a refund if I'm not satisfied
                                    with the course?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        We want you to have a positive learning experience. If
                                        you're not satisfied with the course, you can request a
                                        course transfer or refund within 14 days of the initial
                                        purchase.
                                    </p>
                                </div>
                            </div>
                        </div>
                        
                        <div>
                            <div>
                                <span>
                                    How do I track my progress in the course?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        Our platform provides tracking tools and progress
                                        indicators for each course. You can monitor your progress,
                                        completed lessons, and assessments through your learner
                                        dashboard for the course.
                                    </p>
                                </div>
                            </div>
                        </div>
                        
                        <div>
                            <div>
                                <span>
                                    What if I have technical issues or difficulties with the
                                    course?
                                </span>
                                <div>
                                    <span></span>
                                </div>
                            </div>
                            <div>
                                <div>
                                    <p>
                                        If you encounter technical issues or content-related
                                        difficulties with the course, our support team is
                                        available to assist you. You can reach out to them for
                                        prompt resolution.
                                    </p>
                                </div>
                            </div>
                        </div>

Healthcare Assistant

Healthcare Science Assistant

Emergency Care Assistant

Health & Social Care

Nurse Assistant

Health Sciences Courses (HSC)

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Set up and configure a data science environment, ensuring optimal productivity and efficiency in data analysis workflows.</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Utilize the powerful features of the NumPy package for efficient manipulation and computation of numerical data.</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Harness the capabilities of the Pandas package for data manipulation, exploration, and analysis, enabling insightful decision-making.</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Create visually compelling data visualizations using Matplotlib, mastering both basic and advanced plotting techniques.</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Apply data analysis and visualization techniques to real-world datasets, deriving actionable insights to drive business success.</span></li></ul><div data-id="4afcd14" data-element_type="widget" data-widget_type="text-editor.default" style="color: rgb(35, 35, 35); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(35, 35, 35);"><div data-id="4afcd14" data-element_type="widget" data-widget_type="text-editor.default" style="color: rgb(35, 35, 35); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(35, 35, 35);"><div data-id="4afcd14" data-element_type="widget" data-widget_type="text-editor.default" style="color: rgb(35, 35, 35); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(35, 35, 35);"><div data-id="4afcd14" data-element_type="widget" data-widget_type="text-editor.default" style="color: rgb(35, 35, 35); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(35, 35, 35);"><div data-id="4afcd14" data-element_type="widget" data-widget_type="text-editor.default" style="color: rgb(35, 35, 35); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(35, 35, 35);"><div data-id="4afcd14" data-element_type="widget" data-widget_type="text-editor.default" style="color: rgb(35, 35, 35); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(35, 35, 35);"><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">Embark on a transformative journey into the world of data with our &ldquo;Data Science and Data Analytics&rdquo; course. Designed for aspiring data professionals and analysts, this comprehensive program equips you with the essential tools and techniques to excel in the dynamic field of data science. From setting up your data science environment to mastering powerful packages like NumPy, Pandas, and Matplotlib, this course covers all facets of data analysis and visualization, empowering you to unlock actionable insights and drive informed decision-making.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover the foundations of data science in the introductory modules, where you&rsquo;ll learn to navigate the data science environment and harness the capabilities of essential tools like NumPy and Pandas. Dive deeper into data visualization with comprehensive tutorials on Matplotlib, exploring advanced techniques to create compelling visual representations of your data. Whether you&rsquo;re a novice eager to enter the world of data or an experienced professional seeking to enhance your skill set, this course is your gateway to mastering the art and science of data analytics.</p></div></div></div></div></div></div><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Data Science &nbsp; &nbsp; &nbsp; &nbsp;</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science Environment setup &nbsp; &nbsp; &nbsp; &nbsp;</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Working with the Numpy package &nbsp; &nbsp; &nbsp; &nbsp;</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Working with Pandas Data science package &nbsp; &nbsp; &nbsp; &nbsp;</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Working with Matplotlib Data Science package part &ndash; 1 &nbsp; &nbsp; &nbsp; &nbsp;</strong></li><li style="font-weight: bold; box-sizing: border-box;" fr-original-style="font-weight: bold;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Working with Matplotlib Data Science part &ndash; 2</strong></li></ul>

LearnDrive UK

Engineering Management

Electrical engineering (EE)

Unlock the power of data with our “Data Science and Data Analytics” course. From setting up your data science environment to mastering essential packages like NumPy, Pandas, and Matplotlib, this program empowers you to analyze, visualize, and derive actionable insights from data.





























Online Data Science & Data Analytics Course with Certificate

<section>
						<div>
				<div>
				<div>
			<div>
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				<div>
				<div>
			
				</div>
				<div>
				<div>
					<div><p>Have you ever envisioned molding digital worlds with just lines of code, but felt lost about where to begin? Enter Python For Beginners Part 1 Course, your gateway to the digital realm. Every epic saga has its genesis, and in the world of programming, this course is yours. With Module 01, you'll be introduced to the very essence of Python, laying a robust foundation for what's to come.</p><p>Python is one of the most in-demand programming languages in the world today, and for good reason. It's easy to learn, powerful, and versatile. Whether you want to build websites, create data science applications, or automate tasks, Python can help you do it.</p><p>By Module 02, you won't just be learning about Python, you'll be actively crafting with it, initiating your first Python scripts. It's one thing to know a language, but entirely another to converse fluently. Modules 03 and 04 are all about that fluency. Here, you'll decipher the language of Data Types and Operators, and understand the art of organizing with Data Structures. These modules ensure you're not just coding, but conversing, making your digital canvas come alive.</p><p>However, what's an artist without control over their palette? Module 05, focusing on Control Flow, gives you the reins to guide your program's direction. Just as a river's flow is directed by its banks, with control flow, you decide where your program's logic flows. By the end, you won't just be a passive observer but an active creator, shaping digital landscapes and setting forth on an unforgettable coding adventure.</p><p>Enrol in the Python For Beginners Part 1 Course and boost up your skills!</p></div>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</div></section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2><b> Learning outcome</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Gain proficiency in Python programming language.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Master basic to advanced Python concepts and syntax.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Develop problem-solving skills using Python.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Understand Python data types and how to manipulate them.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Learn to create reusable codes with functions and classes.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
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				<div>
				<div>
			<div>
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			<h2> <b>Why should I take this course?</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Learn a versatile and widely used language for effective coding.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Enhance your problem-solving and logical thinking skills.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Expand your career opportunities in tech, data science, and AI.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Develop a skill set that allows you to automate tasks and analyze data.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Increase your confidence in coding and algorithm development.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
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			<h2> <b>Career Path</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Python Developer</span>
									</li>
								<li>
											<span>
													</span>
										<span>Data Scientist</span>
									</li>
								<li>
											<span>
													</span>
										<span>Machine Learning Engineer</span>
									</li>
								<li>
											<span>
													</span>
										<span>Software Engineer (specialising in Python)</span>
									</li>
								<li>
											<span>
													</span>
										<span>DevOps Engineer (using Python scripting)</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2> <b>Requirements</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>A computer with internet access.</span>
									</li>
								<li>
											<span>
													</span>
										<span>No prior knowledge of Python or programming is required.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Eagerness to learn and explore Python programming.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section><div>
                            <div>
                                <h2>Course Curriculum</h2>
                            </div>
                            			<div>
				

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4"><strong>Module 01: Introduction to the Python for Beginners Course</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Introduction to the Python for Beginners Course</td><td><span>🕐 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 02: Getting Started with Python</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Getting Started with Python</td><td><span>🕐 00:53:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 03: Data Types and Operators</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Types and Operators</td><td><span>🕐 01:54:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 04: Data Structures</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Data Structures</td><td><span>🕐 01:59:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Module 05: Control Flow</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Control Flow</td><td><span>🕐 01:14:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
		                        </div>

Skill Up

Dip yourself in the dynamic world of Python programming with our Python for Beginners Online Course. Master Python's key concepts, its syntax, data types, and much more.

Python For Beginners Part 1

<strong>Get started with using linear algebra in your data science projects</strong>
        <p>Vectorizing your code is an essential skill to make your calculations faster and take advantage of the capabilities of modern machine and deep learning packages. This course will get you up and running with linear algebra fundamentals for data science in Python. 

In this course, you will learn about scalars, vectors, and matrices and the geometrical meaning of these objects. You will also learn how you should use linear algebra in your Python code. In addition to this, you'll be able to perform operations such as addition, subtraction and dot product. As you cover further sections, you'll focus on the different syntactical errors you can encounter while vectorizing your code.  

By the end of this course, you will have gained the skills you need to use linear algebra confidently in your data science projects. 

All code and supporting files for this course are available at - https://github.com/PacktPublishing/Linear-Algebra-for-Data-Science-in-Python</p>
        <h2>What You Will Learn</h2><p>Focus on the addition and subtraction of Matrix <br>Understand errors when adding matrices <br>Learn why linear algebra is useful</p>
        <h2>Audience</h2><p>This course is designed for aspiring data scientists or anyone who wants to learn linear algebra in Python.</p>
        <h2>Approach</h2><p>This course takes you through linear algebra in a systematic manner. It is even packed with step-by-step instructions and working examples to enhance your learning experience.</p>
        <h2>Key Features</h2><p>Learn linear algebra for data science and understand the essential concepts  * Understand matrix, scalars, and vectors and learn how to use them</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/linear-algebra-for-data-science-in-python</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Linear Algebra Fundamentals</h4><p></p>

Linear Algebra

Get started with using linear algebra in your data science projects

Linear Algebra for Data Science in Python

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				<p><span>Register on the Covid19 Awareness Diploma for Healthcare Assistant today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career.</span></p><p><span>The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials.</span></p><p><span>Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as proof of your course completion.</span></p><p><span>The Covid19 Awareness Diploma for Healthcare Assistant is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones.</span></p><p><span>The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly!</span></p><h5><b>What You Get With The Covid19 Awareness Diploma for Healthcare Assistant </b></h5><ul><li><span>Receive a e-certificate upon successful completion of the course</span></li><li><span>Get taught by experienced, professional instructors</span></li><li><span>Study at a time and pace that suits your learning style</span></li><li><span>Get instant feedback on assessments </span></li><li><span>24/7 help and advice via email or live chat</span></li><li><span>Get full tutor support on weekdays (Monday to Friday)</span></li></ul>					</div>
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				<div id="course-home"><div><p><span>The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.</span></p><p><span>You are taught through a combination of</span></p><ul><li><span>Video lessons</span></li><li><span>Online study materials</span></li></ul></div></div>					</div>
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				<p><span>Upon successful completion of the course, you will be able to obtain your course completion e-certificate free of cost. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99.</span></p>					</div>
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				<p><span>The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.</span></p>					</div>
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				<p><span>The online training is open to all students and has no formal entry requirements. To study the Covid19 Awareness Diploma for Healthcare Assistant, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16.</span></p>					</div>
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				<h3>Course Content</h3>

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			<h2>Frequently Asked Questions</h2>
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                                    Are there any prerequisites for taking the course?
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                                        There are no specific prerequisites for this course, nor
                                        are there any formal entry requirements. All you need is
                                        an internet connection, a good understanding of English
                                        and a passion for learning for this course.
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                                    Can I access the course at any time, or is there a set
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                                        You have the flexibility to access the course at any time
                                        that suits your schedule. Our courses are self-paced,
                                        allowing you to study at your own pace and convenience.
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                                        For this course, you will have access to the course
                                        materials for 1 year only. This means you can review the
                                        content as often as you like within the year, even after
                                        you've completed the course. However, if you buy Lifetime
                                        Access for the course, you will be able to access the
                                        course for a lifetime.
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                                    Is there a certificate of completion provided after
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                                        Yes, upon successfully completing the course, you will
                                        receive a certificate of completion. This certificate can
                                        be a valuable addition to your professional portfolio and
                                        can be shared on your various social networks.
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                                    Can I switch courses or get a refund if I'm not satisfied
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                                        We want you to have a positive learning experience. If
                                        you're not satisfied with the course, you can request a
                                        course transfer or refund within 14 days of the initial
                                        purchase.
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                                    How do I track my progress in the course?
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                                        Our platform provides tracking tools and progress
                                        indicators for each course. You can monitor your progress,
                                        completed lessons, and assessments through your learner
                                        dashboard for the course.
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                                    What if I have technical issues or difficulties with the
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                                        If you encounter technical issues or content-related
                                        difficulties with the course, our support team is
                                        available to assist you. You can reach out to them for
                                        prompt resolution.
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Covid19 Awareness Diploma for Healthcare Assistant

<strong>This AWS SageMaker Canvas course will help you become a machine learning expert and will enhance your skills by offering you comprehensive knowledge and the required hands-on experience on this newly launched cloud-based ML tool, by solving real-time industry-based projects, without needing any complex coding expertise.</strong>
        <p>AWS is the #1 cloud-based tool used industry-wide for machine learning projects. You do not need advanced coding expertise that is generally required in the field of machine learning. Nowadays, machine learning models that usually take many days to build are available very quickly in just a few minutes with the help of SageMaker Canvas.

SageMaker Canvas has a small learning curve and you can pick up even advanced concepts very quickly. It is also available as part of AWS Free Tier.

This course is created with students from various backgrounds in mind. Therefore, we start with the fundamentals and work our way up to more advanced topics. In addition, we will work on four projects in the course itself, from beginning to end. By providing you with in-depth knowledge and the necessary hands-on experience on this newly introduced cloud-based ML tool, this course will help you become a machine learning expert and enhance your skills. You won't need any advanced coding knowledge to complete projects that are based on real-world industry problems. Guidance is offered beyond the tool-you will not only learn the software but also important machine learning principles.

By the end of this course, you will be able to build your own machine learning model and get accurate predictions without writing any code using AWS SageMaker Canvas.

All the resources and datasets for this course are available at:  https://github.com/PacktPublishing/No-Code-Machine-Learning-Using-Amazon-AWS-SageMaker-Canvas</p>
        <h2>What You Will Learn</h2><p>Learn the basics of machine learning<br>Get introduced to AWS SageMaker<br>Use the Banknote Authentication dataset to predict data<br>Use the SMS Spam Collection dataset to predict data<br>Use the Customer Churn Prediction 2020 dataset to predict data<br>Use the Wine Quality dataset to predict data</p>
        <h2>Audience</h2><p>This course is designed for students who want to enter the machine learning domain but don't have coding expertise, or anybody in general who wants to know what machine learning is and how to use it professionally. You won't need any high-configuration computer to learn this tool. All you need is any system with internet connectivity and a basic understanding of machine learning.</p>
        <h2>Approach</h2><p>This course is designed keeping in mind students from all backgrounds. Hence, we cover everything from the basics and gradually progress toward advanced topics. We will also take live industry projects and take every step from start to end in the course.</p>
        <h2>Key Features</h2><p>Machine learning on Amazon's AWS SageMaker Canvas without writing any code * Learn how to train and test the ML models and get accurate predictions * Work on four live projects with a sample dataset</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/No-Code-Machine-Learning-Using-Amazon-AWS-SageMaker-Canvas</p>
        <h2>About the Author</h2><h3>Prince Patni</h3><p>Prince Patni is a software developer who specializes in business intelligence and data science. He has studied engineering, worked in four multinational corporations, and has been all over the world for business. He is currently working as an analyst/developer in a reputable organization. He enjoys both teaching and learning. He is here to offer his knowledge of data analytics, data visualization, business intelligence, data science, and other aspects of software development.</p>
        <h2>Course Outline</h2><h4>1. Introduction to Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Introduction</p><p style='margin: 0'>Welcome to the course! In this video, we will get introduced to the course and its learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. What Is Machine Learning?</p><p style='margin: 0'>In this video, we will understand machine learning.</p></td></tr></tbody></table><br><h4>2. Introduction to AWS</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Is Amazon Web Services (AWS)?</p><p style='margin: 0'>In this video, we will understand Amazon Web Services (AWS).</p></td></tr><tr><td style='padding: 15px;'><p>2. Signing into AWS Console</p><p style='margin: 0'>In this video, you will learn how to sign into the AWS Console.</p></td></tr></tbody></table><br><h4>3. Introduction to SageMaker</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Is SageMaker and How Is It Used for Machine Learning?</p><p style='margin: 0'>In this video, we will understand SageMaker and how it is used for machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. What Is SageMaker Canvas?</p><p style='margin: 0'>In this video, we will understand SageMaker Canvas.</p></td></tr></tbody></table><br><h4>4. Setup</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. SageMaker Domain and User Setup</p><p style='margin: 0'>In this video, we will work on the SageMaker domain and user setup.</p></td></tr><tr><td style='padding: 15px;'><p>2. Set Up Data in S3 Buckets for Use in SageMaker</p><p style='margin: 0'>In this video, you will learn how to set up data in S3 Buckets for use in SageMaker.</p></td></tr></tbody></table><br><h4>5. SageMaker Canvas Interface Walkthrough</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Navigating in SageMaker Canvas</p><p style='margin: 0'>In this video, we will explore SageMaker Canvas.</p></td></tr></tbody></table><br><h4>6. Project 1 - Banknote Authentication</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Adding Training Data</p><p style='margin: 0'>In this video, you will learn how to add training data.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building and Using the Model for Prediction</p><p style='margin: 0'>In this video, you will learn how to build and use the model for prediction.</p></td></tr><tr><td style='padding: 15px;'><p>3. Predict Single and Batch Dataset</p><p style='margin: 0'>In this video, you will learn how to predict single and batch dataset.</p></td></tr><tr><td style='padding: 15px;'><p>4. Validating Accuracy of Batch Predictions</p><p style='margin: 0'>In this video, you will learn how to validate the accuracy of batch predictions.</p></td></tr></tbody></table><br><h4>7. Project 2 - Spam SMS Detection</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Adding Train and Test Data</p><p style='margin: 0'>In this video, you will learn how to add train and test data.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building and Using the Model for Prediction</p><p style='margin: 0'>In this video, you will learn how to build and use the model for prediction.</p></td></tr><tr><td style='padding: 15px;'><p>3. Predicting Data and Validating Accuracy</p><p style='margin: 0'>In this video, you will learn how to predict data and validate the accuracy.</p></td></tr></tbody></table><br><h4>8. Project 3 - Customer Churn Prediction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Adding Data</p><p style='margin: 0'>In this video, you will learn how to add the data.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building the Model</p><p style='margin: 0'>In this video, you will learn how to build the model.</p></td></tr><tr><td style='padding: 15px;'><p>3. Performing and Validating Predictions</p><p style='margin: 0'>In this video, you will learn how to perform and validate predictions.</p></td></tr></tbody></table><br><h4>9. Project 4 - Wine Quality Prediction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Adding and Joining Datasets</p><p style='margin: 0'>In this video, you will learn how to add and join datasets.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building the Model</p><p style='margin: 0'>In this video, you will learn how to build the model.</p></td></tr><tr><td style='padding: 15px;'><p>3. Predicting Test Data</p><p style='margin: 0'>In this video, you will learn how to predict test data.</p></td></tr></tbody></table><br><h4>10. Assignment</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. White Wine Quality Prediction</p><p style='margin: 0'>In this video, you need to work on a self-practice assignment using the Wine Quality dataset and perform white wine quality prediction.</p></td></tr></tbody></table><br><h4>11. Other Important Features in SageMaker Canvas</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Versioning</p><p style='margin: 0'>In this video, we will cover versioning.</p></td></tr></tbody></table><br><h4>12. Congratulations and Next Steps</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Getting Datasets for Practice</p><p style='margin: 0'>In this video, you will learn how to get datasets for practice.</p></td></tr><tr><td style='padding: 15px;'><p>2. Getting Help on SageMaker Canvas</p><p style='margin: 0'>In this video, you will learn how to get help on SageMaker Canvas.</p></td></tr><tr><td style='padding: 15px;'><p>3. Congratulations and Thank You</p><p style='margin: 0'>Congratulations, you did it! Thank you for taking this course. Happy learning!</p></td></tr></tbody></table>

Testing

Model

This AWS SageMaker Canvas course will help you become a machine learning expert and will enhance your skills by offering you comprehensive knowledge and the required hands-on experience on this newly launched cloud-based ML tool, by solving real-time industry-based projects, without needing any complex coding expertise.

No-Code Machine Learning Using Amazon AWS SageMaker Canvas

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							<p><b>***With this Coronavirus (COVID-19) Awareness course, get a Personal Hygiene Course completely FREE and prevent yourself from being infected by Coronavirus and other contagious diseases.***</b></p><p><span>Coronavirus (COVID-19) Awareness is yet another 'Teacher's Choice' course from Teachers Training for a complete understanding of the fundamental topics. You are also entitled to exclusive tutor support and a professional CPD-accredited certificate in addition to the special discounted price for a limited time. Just like all our courses, this Coronavirus (COVID-19) Awareness and its curriculum have also been designed by expert teachers so that teachers of tomorrow can learn from the best and equip themselves with all the necessary skills.</span></p><p><span>Consisting of several modules, the course teaches you everything you need to succeed in this profession.</span></p><p><span>The course can be studied part-time. You can become accredited within 2 hours studying at your own pace. Your qualification will be recognised and can be checked for validity on our dedicated website.</span></p>						</div>
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			<h2>Why Choose Teachers Training</h2>		</div>
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			<p>Some of our website features are:</p>		</div>
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			<h2> Entry Requirements
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			<p>No formal entry requirements. You need to have:</p>		</div>
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										<span>A good understanding of the English language</span>
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										<span>Over the age of 16.</span>
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				<section>
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			<h2>Assessment</h2>		</div>
				</div>
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							<p><span>The assessment is straightforward, you need to complete the assignment questions that will be provided to you at the end of the course, you can complete the assignment anytime you want. After you complete and submit your assignment, our tutors will assess your assignment and give you feedback if needed. </span></p><p><span>After your assignment has been assessed and you have passed, you will be qualified and will be able to apply for a course completion certificate.</span></p>						</div>
				</div>
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		</div>
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		</section>
				<section>
						<div>
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			<div>
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			<h2>Certification</h2>		</div>
				</div>
				<div>
				<div>
							<p><b>CPD Certification from The Teachers Training</b></p><p>Successfully completing the MCQ exam of this course qualifies you for a CPD-accredited certificate from The Teachers Training. You will be eligible for both PDF copy and hard copy of the certificate to showcase your achievement however you wish.</p><ul><li>You can get your digital certificate (PDF) for £4.99 only</li><li>Hard copy certificates are also available, and you can get one for only £10.99</li><li>You can get both PDF and Hard copy certificates for just £12.99!</li></ul><p>The certificate will add significant weight to your CV and will give you a competitive advantage when applying for jobs.</p>						</div>
				</div>
					</div>
		</div>
							</div>
		</section>
							</div><table width="100%">				<tbody><tr>
					<td colspan="4">Module 01: Introduction to COVID-19</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to COVID-19</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 02: Understanding of Covid-19 as a Pandemic and How It Is Related to Zoonotic Diseases Like SARS and MERS</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Understanding of Covid-19 as a Pandemic and How It Is Related to Zoonotic Diseases Like SARS and MERS</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 03: The Strike of Pandemic Around the World and Great Britain</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>The Strike of Pandemic Around the World and Great Britain</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 04: Effects of Covid-19 on Human Life</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Effects of Covid-19 on Human Life</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 05: Diagnosis and Treatment</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Diagnosis and Treatment</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 06: Preventions and Social Measures to Be Taken</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Preventions and Social Measures to Be Taken</td>
					<td> </td>
					<td><span><i></i> 00:30:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 07: Artificial Intelligence, Data Science and Technological Solutions against Covid-19</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Artificial Intelligence, Data Science and Technological Solutions against Covid-19</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
				</tbody></table>

The Teachers Training

Certificate in Teaching Program (CTP)

Teaching Online

Teaching & Training

Coronavirus (COVID-19) Awareness

<div>
					<div>
								<section>
						<div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
					</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>
		</section>
							</div>
				</div>
			<h2><b>Course Description</b></h2>
<div>
<p><span>Get instant knowledge from this bite-sized COVID-19 Awareness: Part 2 course. This course is very short and you can complete it within a very short time. In this COVID-19 Awareness: Part 2 course you will get fundamental ideas of COVID-19, the key understanding of diagnosis, treatment and so on. Enrol in this course today and start your instant first step towards learning about the effects of COVID-19 on human life. Learn faster for instant implementation.  </span></p>
</div>
<h2><b>Learning Outcome</b></h2>
<ul>
<li><span>Familiarise with the effects of COVID-19 on human life </span></li>
<li><span>Deepen your understanding of artificial intelligence, data science and technological solutions against COVID-19 </span></li>
<li><span>Learn about the diagnosis and treatment </span></li>
<li><span>Gain an excellent understanding of the preventions and social measures to be taken </span></li>
</ul>
<h2><b>How Much Do Doctors Earn?</b></h2>
<ul>
<li><span>Senior - £100,000 (Apprx.) </span></li>
<li><span>Average - £52,000 (Apprx.) </span></li>
<li><span>Starting - £27,000 (Apprx.) </span></li>
</ul>
<h2><strong>Requirement</strong></h2>
<p>Our<strong> COVID-19 Awareness: Part 2 </strong>is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation.</p><table>				<tbody><tr>
					<td colspan="4">COVID-19 Awareness: Part 2</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Effects of Covid-19 on Human Life</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Diagnosis and Treatment</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Preventions and Social Measures to Be Taken</td>
					<td> </td>
					<td><span><i></i> 00:28:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Artificial Intelligence, Data Science and Technological Solutions against Covid-19</td>
					<td> </td>
					<td><span><i></i> 00:19:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Assignment</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assignment - COVID-19 Awareness: Part 2</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table>

iStudy UK

COVID-19 Awareness: Part 2

<p dir="ltr" id="isPasted" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Python for Data Science and Machine Learning Bootcamp course is a comprehensive and practical online course designed to teach you how to use Python for data science and machine learning.&nbsp;</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">The course comprises several sections, each covering different topics in data science and machine learning. It includes all the essential concepts and tools required to analyse and manipulate data, build machine learning models, and make predictions or decisions.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">It also covers dataset summary statistics and visualisation techniques using libraries like matplotlib and seaborn. You&#39;ll learn about model selection, ensemble approaches, and parameter adjustment. Additionally, you will discover how to export and load machine learning models using libraries such as pickle and joblib.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">This course is suitable for anyone interested in learning Python for data science and machine learning. Whether you are an aspiring data scientist or a professional looking to enhance your data analysis skills, this course will help you achieve your goals.</span></p><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Enrol now to gain instant access to the course materials and start your learning journey. Don&#39;t miss out on the opportunity to acquire one of the most in-demand skills in the market today and become a proficient data scientist and machine learning expert.</span></p><h2 dir="ltr" id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Learning Outcome of Python for Data Science and Machine Learning Bootcamp &nbsp; &nbsp;</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Master fundamental concepts of machine learning and its applications.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Gain proficiency in Python programming for data analysis and manipulation.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Learn to load, read, and manipulate CSV data files using Python libraries.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Understand dataset summary statistics and visualization techniques.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Acquire skills in data preparation and preprocessing.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Explore feature selection techniques and their importance.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Evaluate machine learning algorithms using various techniques and metrics.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Spot check classification and regression algorithms for model selection.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Improve model performance using ensemble methods and parameter tuning.</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Learn to export, save, load, and finalize machine learning models for real-time predictions.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Key Features of the Course</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">A CPD certificate that is recognised worldwide.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">A great online learning experience.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Interesting and unique online materials and activities.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Expert guidance and support from the field leaders.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Access to the study resources anytime you want.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Friendly and helpful customer service and admin support by email, phone, and chat from Monday through Friday.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Get a year-long access to the course.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Who is this course for</span></h2><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); font-size: 16px; box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0); font-size: 16px;">This Python for Data Science and Machine Learning Bootcamp Course is suitable for -</span></p><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Aspiring data scientists and machine learning enthusiasts</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Professionals seeking to enhance their data analysis skills</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Individuals interested in Python programming for data science</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Anyone looking to break into the field of data science and machine learning</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Students pursuing studies in computer science or related fields</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Requirements</span></h2><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Basic understanding of programming concepts</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Familiarity with Python programming language is beneficial but not mandatory</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">No prerequisites; suitable for individuals from any academic background.</li><li dir="ltr" style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Accessible course materials from any internet-enabled device.</li></ul><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">CPD Certificate from Course Gate</span></h2><p dir="ltr" fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for &pound;9 and the hard copy for &pound;15. Also, you can order both PDF and hardcopy certificates for &pound;22.</span></p><h2 dir="ltr" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color:#000000;">Career path</span><span style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><br fr-original-style="" style="box-sizing: border-box;"></span></h2><div style="color: rgb(68, 68, 68); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(68, 68, 68);"><div style="color: rgb(68, 68, 68); box-sizing: border-box;" fr-original-style=" color: rgb(68, 68, 68);"><div data-elementor-type="wp-post" data-elementor-id="357815" data-elementor-post-type="course" fr-original-style="" style="box-sizing: border-box;"><section data-id="5767f71a" data-element_type="section" fr-original-style="" style="box-sizing: border-box;"><div data-id="58784751" data-element_type="column" fr-original-style="" style="box-sizing: border-box;"><div data-id="ce105db" data-element_type="widget" data-widget_type="text-editor.default" style="color: var( --e-global-color-text ); box-sizing: border-box;" fr-original-style=" color: var( --e-global-color-text );"><ul fr-original-style="" style="box-sizing: border-box;"><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Scientist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Machine Learning Engineer</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Business Intelligence Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Engineer</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Research Scientist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Quantitative Analyst</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Artificial Intelligence Specialist</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Data Consultant</li><li style="font-size: 16px; color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="font-size: 16px; color: rgb(0, 0, 0);">Statistician</li></ul></div></div></section></div></div></div>

Course Gate

Machine Learning (ML)

Python for Data Science and Machine Learning Bootcamp online course is suitable for anyone interested in learning Python for data science and machine learning. It is especially ideal for aspiring data scientists and professionals seeking to enhance their data analysis skills. 

Python for Data Science and Machine Learning Bootcamp

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Learn how to become a data scientist, data engineer, data analyst or consultant</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Know the different processes of Data Science, such as data cleaning, processing, wrangling, manipulation</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Discover the usage of R to clean, analyse, and visualise data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">How to write complex R programs for real-life industry situations</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Get data scientist resume tips and land your first job</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Explore the various applications of Machine Learning</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Understand Tidyverse, Operators, Vectors, Lists and their applications</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover data extraction and scraping</span></li></ul><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">Data science and Machine Learning are one of the most popular fields in the world today. Data science is crucial to analysing and developing data-based machine learning models. We will cover programming in R in this course for data science and machine learning.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">R programming is a specialised skill that is in high demand today. We&rsquo;ve designed our&nbsp;<b fr-original-style="" style="box-sizing: border-box;">Data Science and Machine Learning with R</b>&nbsp;course to help you learn fundamentals through advanced R programming. This course teaches you how to effectively use R programming for machine learning or data science. You&rsquo;ll learn how to visualise data by plotting in R, as well as how to clean data.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">You will receive training in machine learning and its many real-world applications while working with R Shiny to create web applications and online interactive dashboards. In other words, you will gain practical, step-by-step knowledge of the R programming language that you may use for business or professional purposes.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">An expert instructor designed this&nbsp;<b fr-original-style="" style="box-sizing: border-box;">&ldquo;Data Science and Machine Learning with R&rdquo;</b>&nbsp;course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">To explore the world of R Programming Language, Enrol Now!</p><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Data Science +ML with R from A-Z</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Getting Started with R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Types and Structures in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Intermediate R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Manipulation in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Visualization in R</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Creating Reports with R Markdown</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Building Webapps with R Shiny</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Machine Learning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Preprocessing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear Regression: A Simple Model</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Exploratory Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear Regression: A Real Model</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Logistic Regression</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Starting a Career in Data Science</strong></li></ul>

The course covers data science and machine learning with R.



Data Science and Machine Learning with R

<strong>This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.</strong>
        <p>Machine learning is the key to development in many areas, such as IT, security, marketing, automation, and even medicine. Without machine learning, it is impossible to build intelligent applications and devices, such as Alexa, Siri, and Google Assistant. This course will help to get familiar with data science and machine learning.

The course starts with an introduction to data science, explaining different terms associated with it. You will also become familiar with machine learning and data science modeling and explore the key differences between model parameters and hyperparameters. Next, you will become familiar with the concepts of machine learning models, such as linear regression, decision trees, random forests, neural networks, and clustering techniques. Towards the end, you will learn how to evaluate machine learning models and learn the best practices to succeed in your data scientist role.

By the end of this course, you will have a solid understanding of data science and machine learning fundamentals.</p>
        <h2>What You Will Learn</h2><p>Become familiar with data science and machine learning terms<br>Distinguish between model parameters and hyperparameters<br>Distinguish between supervised and unsupervised learning<br>Discover how decision trees, bagging, and random forest works<br>Understand the importance of the k-nearest neighbors (KNN) algorithm in machine learning<br>Learn about neural networks and clustering techniques<br>Evaluate the performance of machine learning models</p>
        <h2>Audience</h2><p>This course is designed for students and beginners who want to understand the concepts, statistics, and math behind machine learning algorithms and for those who are curious to solve real-world problems using machine learning and data science. Everything is taught from scratch; hence, there are no prerequisites to get started with this course.</p>
        <h2>Approach</h2><p>With the help of simple explanation, avoiding confusing mathematical notation and jargon, this course covers the fundamentals of data science, machine learning, and data mining from scratch.</p>
        <h2>Key Features</h2><p>Learn the fundamentals of data science, machine learning, and data mining * Learn interesting techniques to evaluate a machine learning model * Discover the best practices to solve real-world problems using 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. Welcome and Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome</p><p style='margin: 0'>This video welcomes you to the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Data Science</p><p style='margin: 0'>This video provides an introduction to data science.</p></td></tr></tbody></table><br><h4>2. Preliminary Requirements to Understand Data Science and Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Understanding Data Science Terms</p><p style='margin: 0'>This video provides an overview of the terms used in data science.</p></td></tr><tr><td style='padding: 15px;'><p>2. Understanding Machine Learning</p><p style='margin: 0'>This video presents an overview of machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>3. Types of Learning</p><p style='margin: 0'>This video explains the different types of learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Understanding Data Science Modeling</p><p style='margin: 0'>This video focuses on data science modeling.</p></td></tr><tr><td style='padding: 15px;'><p>5. Model parameters versus Hyperparameters</p><p style='margin: 0'>This video explains the difference between model parameters and hyperparameters.</p></td></tr></tbody></table><br><h4>3. Machine Learning Models</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How Linear Regression Works</p><p style='margin: 0'>This video demonstrates how linear regression works.</p></td></tr><tr><td style='padding: 15px;'><p>2. How Decision Trees Work</p><p style='margin: 0'>This video demonstrates how decision trees work.</p></td></tr><tr><td style='padding: 15px;'><p>3. How Bagging and Random Forest Work</p><p style='margin: 0'>This video demonstrates the working of bagging and random forest.</p></td></tr><tr><td style='padding: 15px;'><p>4. How Support Vector Machine Works</p><p style='margin: 0'>This video demonstrates the working of support vector machines.</p></td></tr><tr><td style='padding: 15px;'><p>5. Neural Networks- Part 1</p><p style='margin: 0'>This video is the first part of a two-part video that explains neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>6. Neural Networks- Part 2</p><p style='margin: 0'>This is the second part of the two-part video that explains neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>7. How Logistic Regression Works</p><p style='margin: 0'>This video demonstrates the working of logistic regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. How k-nearest neighbour (KNN) Works</p><p style='margin: 0'>This video focuses on KNN and explains how it works.</p></td></tr><tr><td style='padding: 15px;'><p>9. Clustering Techniques</p><p style='margin: 0'>This video explains the different clustering techniques.</p></td></tr></tbody></table><br><h4>4. Model Performance</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Evaluating Models Performance</p><p style='margin: 0'>This video demonstrates how to evaluate the performance of the models.</p></td></tr></tbody></table><br><h4>5. Best Practices for a Data Scientist</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Best Practices for a Data Scientist</p><p style='margin: 0'>This video explains the best practices used by data scientists.</p></td></tr></tbody></table>

Algorithms

This course starts with the basics of data science and gradually moves towards explaining the concepts of machine learning and various data science algorithms.

Mastering Data Science and Machine Learning Fundamentals

<p>Feeling Stuck in Your Career? The Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle is Your Skill-Building Solution. This exceptional collection of <strong>30 premium courses</strong> is designed to encourage growth and improve your career opportunities. Suited to meet different interests and goals, the  Python for Data Science and Machine Learning bundle provides an engaging learning experience, helping you learn skills across various disciplines.</p><p>With Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle, you'll have a personalised journey that aligns with your career goals and interests. This comprehensive package helps you confidently tackle new challenges, whether entering a new field or enhancing your existing knowledge. The  Python for Data Science and Machine Learning bundle is your gateway to expanding your career options, increasing job demand, and enhancing your skill set.</p><p>By enrolling in this bundle, you'll receive complimentary PDF certificates for all courses, adding value to your resume at no extra cost. Develop key skills and achieve important progress in your career and personal development. Start your journey today and experience the transformative impact of the Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! bundle on your job life and career growth!</p><p><strong>This Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle Comprises the Following CPD Accredited Courses:</strong></p><ul><li><strong>Course 01</strong>: Machine Learning Basics</li><li><strong>Course 02</strong>: Azure Machine Learning</li><li><strong>Course 03</strong>: Python Intermediate Training</li><li><strong>Course 04</strong>: Python Data Science</li><li><strong>Course 05</strong>: 2021 Data Science &amp; Machine Learning with R from A-Z</li><li><strong>Course 06</strong>: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 07</strong>: Learn Python, JavaScript, and Microsoft SQL for Data science</li><li><strong>Course 08</strong>: Spatial Data Visualisation and Machine Learning in Python</li><li><strong>Course 09</strong>: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 10</strong>: Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li><strong>Course 11</strong>: Deep Learning &amp; Neural Networks Python - Keras</li><li><strong>Course 12</strong>: The Complete Python 3 Course: Beginner to Advanced!</li><li><strong>Course 13</strong>: Building Big Data Pipelines with PySpark MongoDB and Bokeh</li><li><strong>Course 14</strong>: Clinical Data Management with SAS Programming</li><li><strong>Course 15</strong>: Certificate in Data Entry and Management</li><li><strong>Course 16</strong>: Quick Data Science Approach from Scratch</li><li><strong>Course 17</strong>: SQL for Data Science, Data Analytics and Data Visualization</li><li><strong>Course 18</strong>: Programming AutoCAD with SQL Server Database Using C#</li><li><strong>Course 19</strong>: Big Data Analytics with PySpark Power BI and MongoDB</li><li><strong>Course 20</strong>: Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau</li><li><strong>Course 21</strong>: Develop Big Data Pipelines with R, Sparklyr &amp; Power BI</li><li><strong>Course 22</strong>: Data Center Training Essentials: Power &amp; Electrical</li><li><strong>Course 23</strong>: Business Intelligence and Data Mining</li><li><strong>Course 24</strong>: Data Analysis In Excel</li><li><strong>Course 25</strong>: CV Writing and Job Searching</li><li><strong>Course 26</strong>: Learn to Level Up Your Leadership</li><li><strong>Course 27</strong>: Networking Skills for Personal Success</li><li><strong>Course 28</strong>: Ace Your Presentations: Public Speaking Masterclass</li><li><strong>Course 29</strong>: Learn to Make a Fresh Start in Your Life</li><li><strong>Course 30</strong>: Motivation - Motivating Yourself &amp; Others</li></ul><p><strong>What will make you stand out?</strong></p><p>Upon completion of this online Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! Bundle, you will gain the following:</p><ul><li>CPD QS Accredited Proficiency with this  Python for Data Science and Machine Learning  Bundle </li><li>After successfully completing the  Python for Data Science and Machine Learning bundle, you will receive a FREE PDF Certificate from REED as evidence of your newly acquired abilities.</li><li>Lifetime access to the whole collection of learning materials of this  Python for Data Science and Machine Learning       Bundle</li><li>The online test with immediate results </li><li>You can study and complete the  Python for Data Science and Machine Learning bundle at your own pace.</li><li>Study for the  Python for Data Science and Machine Learning bundle using any internet-connected device, such as a computer, tablet, or mobile device.</li></ul><p>The  Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! bundle is a premier learning resource, with each course module holding respected CPD accreditation, symbolising exceptional quality. The content is packed with knowledge and is regularly updated to ensure it remains relevant. This bundle offers not just education but a constantly improving learning experience designed to enrich both your personal and professional development.</p><p>Advance the future of learning with the   Python for Data Science and Machine Learning bundle, a comprehensive, complete collection of 30 courses. Each course in the  Python for Data Science and Machine Learning bundle has been handpicked by our experts to provide a broad range of learning opportunities. Together, these modules form an important and well-rounded learning experience.</p><p>Our mission is to deliver high-quality, accessible education for everyone. Whether you are starting your career, switching industries, or enhancing your professional skills, the   Python for Data Science and Machine Learning bundle offers the flexibility and convenience to learn at your own pace. Make the   Python for Data Science and Machine Learning package your trusted partner in your lifelong learning journey.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        300 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>The Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses! is perfect for:</p><ul><li>Expand your knowledge and skillset for a fulfilling career with the  Python for Data Science and Machine Learning bundle.</li><li>Become a more valuable professional by earning CPD certification and mastering in-demand skills with the  Python for Data Science and Machine Learning bundle.</li><li>Discover your passion or explore new career options with the diverse learning opportunities in the  Python for Data Science and Machine Learning bundle.</li><li>Learn on your schedule, in the comfort of your home - the  Python for Data Science and Machine Learning bundle offers ultimate flexibility for busy individuals.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You are warmly invited to register for this bundle. Please be aware that no formal entry requirements or qualifications are necessary. This curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Gain a wide range of skills across various fields, improve your problem-solving capabilities, and keep current with industry trends. Perfect for those aiming for career advancement, exploring a new professional direction, or pursuing personal growth. Begin your journey with the  Python for Data Science and Machine Learning bundle.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Certificates</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Compliance Central

Python for Data Science and Machine Learning-(30 in 1) CPD Accredited Courses!

<h2 style="color: var(--text-heading); box-sizing: border-box;" id="isPasted" fr-original-style="color: var(--text-heading);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">This course at a glance</span></strong></h2><ul style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Accredited by CPDQE</li><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Learn how to clean, process, wrangle, and manipulate data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Know how to create a resume for a data science job</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Get a grasp of NumPy by learning how to use it for numerical data</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Uncover the secret of different Python libraries, such as Pandas, Matplotlib, SciPy and many more</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Explore machine learning concepts and algorithms</span></li><li fr-original-style="" style="box-sizing: border-box;"><span style="color: rgb(38, 99, 82) !important; box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">Discover developing custom data solutions</span></li></ul><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(38, 99, 82) !important;">You can create various applications for science, math, machine learning, and data science with Python. As a high-level, general-purpose programming language, Python is quite English-like and easy to learn. This&nbsp;<b fr-original-style="" style="box-sizing: border-box;">Python for Data Science and Machine Learning</b>&nbsp;course will show you how to use Python for machine learning and data science.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">During the course, you will master every useful Python skill you need for data science and machine learning. If you find it challenging to write complex Python programs, we&rsquo;ll show you how to do so with our step-by-step instructions based on actual work contexts. You&rsquo;ll also learn how to run the regression, classification, and clustering and the specifics of supervised and unsupervised machine learning. Moreover, this course covers how statistics work for data science, hypothesis testing, and much more.</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">An expert instructor designed this&nbsp;<b fr-original-style="" style="box-sizing: border-box;">&ldquo;Python for Data Science and Machine Learning&rdquo;</b>&nbsp;course to provide you with the best learning experience possible. Our goal is for you to learn and apply the lessons from this course to improve your skills.&nbsp;</p><p style="color: rgb(38, 99, 82) !important; font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(38, 99, 82) !important;">To become a master of Python, Enrol Now!</p><h3 style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Accredited by</span></h3><p style="color: rgb(15, 21, 26); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);">This course is <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPDQE&nbsp;</strong>accredited, which serves as an impactful mechanism for skill enhancement.&nbsp;</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">Continuing Professional Development (CPD) stands as a crucial, widely acknowledged method that aids individuals, organisations, and entire industries in staying current with their skills and knowledge.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);">CPD not only facilitates the elevation of global standards and benchmarks, aligning with the growing forces of globalisation and consumer expectations but also transforms learning into a deliberate and proactive process. It motivates professionals to uphold a consistently high level of performance and demonstrates their unwavering commitment to a specific job role or profession.</p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><br fr-original-style="" style="box-sizing: border-box;"></p><p style="color: rgb(0, 0, 0); font-size: var(--font-size); box-sizing: border-box;" fr-original-style=" color: rgb(0, 0, 0);"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"><span style="font-size: 24px; box-sizing: border-box;" fr-original-style="font-size: 24px;">Course Curriculum</span></strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li id="isPasted" fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Python for Data Science &amp; Machine Learning from A-Z</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science &amp; Machine Learning Concepts</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python For Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Statistics for Data Science</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Probability and Hypothesis Testing</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">NumPy Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Pandas Data Analysis</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Python Data Visualization</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Introduction to Machine Learning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Loading &amp; Exploration</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Cleaning</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Feature Selecting and Engineering</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Linear and Logistic Regression</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K Nearest Neighbours</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Decision Trees</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Ensemble Learning and Random Forests</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Support Vector Machines</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">K-Means</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">PCA</strong></li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Science Career</strong></li></ul>

This course aims to teach you how to use Python for machine learning and data science.



Python for Data Science and Machine Learning

<strong>In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.</strong>
        <p>The course covers practical issues in statistical computing that include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R programming to mastery.

We understand that theory is important to build a solid foundation, we also understand that theory alone isn't going to get the job done so that's why this course is packed with practical hands-on examples that you can follow step by step. Even if you already have some coding experience, or want to learn about the advanced features of the R programming language, this course is for you!

R coding experience is either required or recommended in job postings for data scientists, machine learning engineers, big data engineers, IT specialists, database developers, and much more. Adding R coding language skills to your resume will help you in any one of these data specializations requiring mastery of statistical techniques.

By the end of the course, you'll be a professional data scientist with R and confidently apply for jobs and will feel good knowing that you have the skills and knowledge to back it up.

All resources are placed here: https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-with-R-from-A-Z-Course-Updated-for-2021-</p>
        <h2>What You Will Learn</h2><p>Learn data cleaning, processing, wrangling, and manipulation<br>Learn plotting in R (graphs, charts, plots, histograms, and more)<br>How to create a resume and land your first job as a data scientist<br>Learn machine learning and its various practical applications<br>Learn data and file management in R<br>Use R to clean, analyze, and visualize data</p>
        <h2>Audience</h2><p>This course is designed for beginners who want to learn about data science and machine learning. No prior knowledge of R is required.</p>
        <h2>Approach</h2><p>This course takes you through data analysis in R, data visualization in R, and machine learning with hands-on step-by-step training.</p>
        <h2>Key Features</h2><p>Learn data extraction and web scraping * Learn to build custom data solutions * Learn automating dynamic report generation</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Data-Science-and-Machine-Learning-with-R-from-A-Z-Course-Updated-for-2021-</p>
        <h2>About the Author</h2><h3>Juan E. Galvan</h3><p>Juan E. Galvan has been an entrepreneur since grade school. His background is in the tech space from digital marketing, e-commerce, web development to programming. He believes in continuous education with the best of a university degree without all the downsides of burdensome costs and inefficient methods. He looks forward to helping people expand their skillsets.</p>
        <h2>Course Outline</h2><h4>1. Data Science and Machine Leaning Course Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Science and Machine Learning Introduction Section Overview</p><p style='margin: 0'>This video explains data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Data Science?</p><p style='margin: 0'>This video explains the concept of data science.</p></td></tr><tr><td style='padding: 15px;'><p>3. Machine Learning Overview</p><p style='margin: 0'>This video gives an overview of machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Science + Machine Learning Marketplace</p><p style='margin: 0'>This video explains the various trends and job opportunities in data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>5. Who is this Course For?</p><p style='margin: 0'>This video explains the audience for this course.</p></td></tr><tr><td style='padding: 15px;'><p>6. Data Science and Machine Learning Job Opportunities</p><p style='margin: 0'>This video explains the various job opportunities in data science and machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Science Job Roles</p><p style='margin: 0'>This video explains the various job roles for data science.</p></td></tr></tbody></table><br><h4>2. Getting Started with R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Getting Started with R</p><p style='margin: 0'>This video explains the concept of R.</p></td></tr><tr><td style='padding: 15px;'><p>2. R Basics</p><p style='margin: 0'>This video explains the R basics.</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Files</p><p style='margin: 0'>This video explains working with files in R.</p></td></tr><tr><td style='padding: 15px;'><p>4. R Studio</p><p style='margin: 0'>This video explains R studio.</p></td></tr><tr><td style='padding: 15px;'><p>5. Tidyverse Overview</p><p style='margin: 0'>This video provides an overview of tidyverse.</p></td></tr><tr><td style='padding: 15px;'><p>6. Additional Resources</p><p style='margin: 0'>This video shares some additional resources that will help you get along with the course.</p></td></tr></tbody></table><br><h4>3. Data Types and Structures in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Types and Structures in R Section Overview</p><p style='margin: 0'>This video explains data types and structures in R.</p></td></tr><tr><td style='padding: 15px;'><p>2. Basic Types</p><p style='margin: 0'>This video explains basic types in R.</p></td></tr><tr><td style='padding: 15px;'><p>3. Vectors - Part One</p><p style='margin: 0'>This video introduces you to vectors.</p></td></tr><tr><td style='padding: 15px;'><p>4. Vectors - Part Two</p><p style='margin: 0'>This video explains how to interact with R using vectors.</p></td></tr><tr><td style='padding: 15px;'><p>5. Vectors: Missing Values</p><p style='margin: 0'>This video explains vector missing values.</p></td></tr><tr><td style='padding: 15px;'><p>6. Vectors: Coercion</p><p style='margin: 0'>This video explains coercion.</p></td></tr><tr><td style='padding: 15px;'><p>7. Vectors: Naming</p><p style='margin: 0'>This video explains vector naming.</p></td></tr><tr><td style='padding: 15px;'><p>8. Vectors: Miscellaneous</p><p style='margin: 0'>This video explains miscellaneous yet important concepts in vectors.</p></td></tr><tr><td style='padding: 15px;'><p>9. Working with Matrices</p><p style='margin: 0'>This video explains working with matrices.</p></td></tr><tr><td style='padding: 15px;'><p>10. Working with Lists</p><p style='margin: 0'>This video explains working with lists.</p></td></tr><tr><td style='padding: 15px;'><p>11. Introduction to Data Frames</p><p style='margin: 0'>This video explains introduction to data frames.</p></td></tr><tr><td style='padding: 15px;'><p>12. Creating Data Frames</p><p style='margin: 0'>This video explains creating data frames.</p></td></tr><tr><td style='padding: 15px;'><p>13. Data Frames: Helper Functions</p><p style='margin: 0'>This video explains data frames helper functions.</p></td></tr><tr><td style='padding: 15px;'><p>14. Data Frames: Tibbles</p><p style='margin: 0'>This video explains Tibbles in data frames.</p></td></tr></tbody></table><br><h4>4. Intermediate R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Intermedia R Section Introduction</p><p style='margin: 0'>This video introduces you to R section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Relational Operators</p><p style='margin: 0'>This video explains relational operators.</p></td></tr><tr><td style='padding: 15px;'><p>3. Logical Operators</p><p style='margin: 0'>This video explains logical operators,</p></td></tr><tr><td style='padding: 15px;'><p>4. Conditional Statements</p><p style='margin: 0'>This video explains conditional statements.</p></td></tr><tr><td style='padding: 15px;'><p>5. Working with Loops</p><p style='margin: 0'>This video explains working with loops.</p></td></tr><tr><td style='padding: 15px;'><p>6. Working with Functions</p><p style='margin: 0'>This video explains working with functions in R.</p></td></tr><tr><td style='padding: 15px;'><p>7. Working with Packages</p><p style='margin: 0'>This video explains working with packages.</p></td></tr><tr><td style='padding: 15px;'><p>8. Working with Factors</p><p style='margin: 0'>This video explains working with factors in R.</p></td></tr><tr><td style='padding: 15px;'><p>9. Dates and Times</p><p style='margin: 0'>This video explains dates and times in R.</p></td></tr><tr><td style='padding: 15px;'><p>10. Functional Programming</p><p style='margin: 0'>This video explains functional programming.</p></td></tr><tr><td style='padding: 15px;'><p>11. Data Import/Export</p><p style='margin: 0'>This video explains how to import and export data in R.</p></td></tr><tr><td style='padding: 15px;'><p>12. Working with Databases</p><p style='margin: 0'>This video explains working with databases.</p></td></tr></tbody></table><br><h4>5. Data Manipulation in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Manipulation Section Introduction</p><p style='margin: 0'>This video introduces you to data manipulation.</p></td></tr><tr><td style='padding: 15px;'><p>2. Tidy Data</p><p style='margin: 0'>This video explains tidy data.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Pipe Operator</p><p style='margin: 0'>This video explains pipe operator.</p></td></tr><tr><td style='padding: 15px;'><p>4. {dplyr}: The Filter Verb</p><p style='margin: 0'>This video explains the filter verb.</p></td></tr><tr><td style='padding: 15px;'><p>5. {dplyr}: The Select Verb</p><p style='margin: 0'>This video explains the select verb</p></td></tr><tr><td style='padding: 15px;'><p>6. {dplyr}: The Mutate Verb</p><p style='margin: 0'>This video explains the mutate verb.</p></td></tr><tr><td style='padding: 15px;'><p>7. {dplyr}: The Arrange Verb</p><p style='margin: 0'>This video explains the purpose of arrange verb.</p></td></tr><tr><td style='padding: 15px;'><p>8. {dplyr}: The Summarize Verb</p><p style='margin: 0'>This video explains the summarize verb.</p></td></tr><tr><td style='padding: 15px;'><p>9. Data Pivoting: {tidyr}</p><p style='margin: 0'>This video explains data pivoting.</p></td></tr><tr><td style='padding: 15px;'><p>10. String Manipulation: {stringr}</p><p style='margin: 0'>This video explains string manipulation.</p></td></tr><tr><td style='padding: 15px;'><p>11. Web Scraping: {rvest}</p><p style='margin: 0'>This video explains web scraping.</p></td></tr><tr><td style='padding: 15px;'><p>12. JSON Parsing: {jsonlite}</p><p style='margin: 0'>This video explains JSON parsing.</p></td></tr></tbody></table><br><h4>6. Data Visualization in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Visualization in R Section Introduction</p><p style='margin: 0'>This video explains data visualization in R.</p></td></tr><tr><td style='padding: 15px;'><p>2. Getting Started with Data Visualization in R</p><p style='margin: 0'>This video explains getting started with data visualization in R.</p></td></tr><tr><td style='padding: 15px;'><p>3. Aesthetics Mappings</p><p style='margin: 0'>This vide explains aesthetics mappings.</p></td></tr><tr><td style='padding: 15px;'><p>4. Single Variable Plots</p><p style='margin: 0'>This video focuses on single variable plots.</p></td></tr><tr><td style='padding: 15px;'><p>5. Two Variable Plots</p><p style='margin: 0'>This video explains two variable plots.</p></td></tr><tr><td style='padding: 15px;'><p>6. Facets, Layering, and Coordinate Systems</p><p style='margin: 0'>This video explains facets, layering, and coordinate systems.</p></td></tr><tr><td style='padding: 15px;'><p>7. Styling and Saving</p><p style='margin: 0'>This video explains styling and saving.</p></td></tr></tbody></table><br><h4>7. Creating Reports with R Markdown</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to R Markdown</p><p style='margin: 0'>This video is an introduction to R.</p></td></tr></tbody></table><br><h4>8. Building Webapps with R Shiny</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to R Shiny</p><p style='margin: 0'>This video is an introduction to R Shiny</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating a Basic R Shiny App</p><p style='margin: 0'>This video helps you to create a basic R shiny app.</p></td></tr><tr><td style='padding: 15px;'><p>3. Other Examples with R Shiny</p><p style='margin: 0'>This video gives various examples with R shiny.</p></td></tr></tbody></table><br><h4>9. Introduction to Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Machine Learning Part One</p><p style='margin: 0'>This video is an introduction to machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Machine Learning Part Two</p><p style='margin: 0'>This video explains the different approaches in machine learning.</p></td></tr></tbody></table><br><h4>10. Data Preprocessing</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Data Preprocessing Introduction</p><p style='margin: 0'>This video introduces you to data preprocessing.</p></td></tr><tr><td style='padding: 15px;'><p>2. Data Preprocessing</p><p style='margin: 0'>This video explains some of the practical ways of data preprocessing.</p></td></tr></tbody></table><br><h4>11. Linear Regression: A Simple Model</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Linear Regression: A Simple Model Introduction</p><p style='margin: 0'>This video explains a simple linear regression model.</p></td></tr><tr><td style='padding: 15px;'><p>2. A Simple Model</p><p style='margin: 0'>This video demonstrates a practical application of machine learning.</p></td></tr></tbody></table><br><h4>12. Exploratory Data Analysis</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Exploratory Data Analysis Introduction</p><p style='margin: 0'>This video gives an introduction to exploratory data analysis.</p></td></tr><tr><td style='padding: 15px;'><p>2. Hands-on Exploratory Data Analysis</p><p style='margin: 0'>This video explains hands-on exploratory data analysis.</p></td></tr></tbody></table><br><h4>13. Linear Regression - a Real Model</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Linear Regression - Real Model Section Introduction</p><p style='margin: 0'>This video explains linear regression - a real model.</p></td></tr><tr><td style='padding: 15px;'><p>2. Linear Regression in R - Real Model</p><p style='margin: 0'>This video explains linear regression in R.</p></td></tr></tbody></table><br><h4>14. Logistic Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Logistic Regression</p><p style='margin: 0'>This video introduces you to logistic regression.</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression in R</p><p style='margin: 0'>This video explains logistic regression in R.</p></td></tr></tbody></table><br><h4>15. Starting a Career in Data Science</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Starting a Data Science Career Section Overview</p><p style='margin: 0'>This video explains section overview of data science and career.</p></td></tr><tr><td style='padding: 15px;'><p>2. Creating a Data Science Resume</p><p style='margin: 0'>This video explains creating a data science resume.</p></td></tr><tr><td style='padding: 15px;'><p>3. Getting Started with Freelancing</p><p style='margin: 0'>This vide explains how to get started with freelancing.</p></td></tr><tr><td style='padding: 15px;'><p>4. Top Freelance Websites</p><p style='margin: 0'>This video focuses on top freelancing websites.</p></td></tr><tr><td style='padding: 15px;'><p>5. Personal Branding</p><p style='margin: 0'>This video explains personal branding.</p></td></tr><tr><td style='padding: 15px;'><p>6. Networking Do's and Don'ts</p><p style='margin: 0'>This video explains networking do's and don'ts.</p></td></tr><tr><td style='padding: 15px;'><p>7. Setting Up a Website</p><p style='margin: 0'>This video explains how to set up a website.</p></td></tr></tbody></table>

In this practical, hands-on course, you'll learn how to use R for effective data analysis and visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

Data Science and Machine Learning with R from A-Z Course [Updated for 2021]

Data Science and Machine Learning using Python : A Bootcamp

Get involved in a learning adventure, mastering R from foundational basics to advanced techniques. This course is a gateway to the realm of data science. Explore statistical machine learning models and intricacies of deep learning and create interactive Shiny apps. Unleash the power of R and elevate your proficiency in data-driven decision-making.

R Ultimate 2023 - R for Data Science and Machine Learning

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					<div><p>Python has steadily ascended the ranks in the tech world, becoming the go-to language for Data Science and Machine Learning pursuits. Its versatility and ease of use make it an optimal choice for those aspiring to delve into the intricate realms of data analysis and algorithm development. By integrating Python into the curriculum, this course ensures that learners are not only familiar with the language but also proficient in its application to Data Science and Machine Learning tasks.</p><p>Data Science, on the other hand, stands as the backbone of today's analytical advancements. With the ever-growing quantum of data, there's a dire need for professionals who can sift through this data deluge and extract meaningful insights. This course simplifies complex topics, making it easier for learners to grasp the intricate mechanics of data manipulation, processing, and analysis. By emphasizing Python's role in Data Science, the course underscores its significance in modern-day data operations.</p><p>Lastly, Machine Learning represents the pinnacle of automated data analysis. With the power of Python and the methodologies of Data Science, Machine Learning enables systems to learn from data and make decisions. This course elucidates these concepts, teaching learners to design, implement, and test Machine Learning algorithms. The synergy of Python, Data Science, and Machine Learning in this curriculum ensures that, by the end, learners are well-equipped to tackle any data challenge that comes their way.</p></div>
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										<span>Develop real-time prediction models using trained datasets.</span>
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			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2> <b>Why should I take this course?</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Stay ahead in the rapidly growing field of Data Science and Machine Learning.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Enhance career opportunities in the tech and data sectors.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Learn to use Python, a leading programming language in Data Science.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Address real-world business challenges using data analysis.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Achieve proficiency in handling and visualizing large datasets.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
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				<div>
			<h2> <b>Career Path</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Data Scientist</span>
									</li>
								<li>
											<span>
													</span>
										<span>Machine Learning Engineer</span>
									</li>
								<li>
											<span>
													</span>
										<span>Python Data Analyst</span>
									</li>
								<li>
											<span>
													</span>
										<span>Predictive Modeler</span>
									</li>
								<li>
											<span>
													</span>
										<span>Data Visualization Expert</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2> <b>Requirements</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Basic understanding of programming concepts.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Eagerness to learn Data Science and Machine Learning.</span>
									</li>
								<li>
											<span>
													</span>
										<span>A computer with internet access.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>

Master Data Science and Machine Learning with Python to transform data-driven decisions in businesses.

Data Science & Machine Learning with Python

<strong>In the fast-paced world of data science and machine learning, you have to stay up-to-date and keep ahead of the competition. For this, you have to constantly be on the lookout for the latest trends in tools and techniques for data science and machine learning. You don't want to miss out on the latest trend and the tool of the future! Right now, that tool is the Julia programming language. It's the hot new language that all ML and data science experts are very excited about. Learning Julia will open up several doors for you in your career!</strong>
        <p>The objective of this course is to give you a strong foundation needed to excel in Julia and learn the core of the language as well as the applied side in the shortest amount of time possible.

We won't waste time with the theory of why Julia is fast. We will jump right into the details and start coding. You will quickly realize how easy it is to learn this state-of-the-art and promising language. You will see how you can start using Julia to excel in your current job without moving the whole stack to Julia immediately.

After explaining the basic concepts, we jump to case studies in data science and then machine learning. We apply both traditional machine learning models and then get to deep learning. You will see how Julia can help you create deep learning models from scratch in just a few lines of code and then move on to the state-of-the-art models without spending too much time.

This way, you get to learn the most important concepts in this subject in the shortest amount of time possible without having to deal with the details of the less relevant topics. Once you have developed an intuition of the important stuff, you can then learn the latest and greatest models even on your own!

By the end of the course, you will have a strong understanding of Julia programming language fundamentals.
The code files are available here: https://github.com/PacktPublishing/Julia-Programming-Language---From-Zero-to-Expert-</p>
        <h2>What You Will Learn</h2><p>Learn coding in Julia programming language<br>Use DataFrames (equivalent to Pandas) in Julia<br>Create ML models from scratch in a way that helps you make modifications easily<br>Learn data wrangling with Julia<br>Use Julia to perform data manipulation, Apache Arrow, grouping, and analysis<br>Classify using decision trees and random forests</p>
        <h2>Audience</h2><p>This course is for all levels of data science and machine learning practitioners aiming to enhance their abilities and skill level in DS and ML. Developers who want to know how to harness the power of big data can also go for this course.<br><br>A basic understanding of programming is a must. Understanding Python, basic data science (reading CSVs and so on), and basic concepts of deep learning (such as classification) is not necessary but would be helpful.</p>
        <h2>Approach</h2><p>This course follows a code-oriented and case-study-based approach. In this course, you will learn by practice as every concept explanation is followed by practical implementation.</p>
        <h2>Key Features</h2><p>Learn the syntax of Julia and its differences from Python * Learn machine learning models, both traditional and deep * Explore data science case studies, including analysis and clustering</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Julia-Programming-Language---From-Zero-to-Expert-</p>
        <h2>About the Author</h2><h3>Dr. Mohammad Nauman</h3><p>Dr. Mohammad Nauman has a PhD in computer science and a PostDoc from the Max Planck Institute for software systems. He has been programming since early 2000 and has worked with many different languages, tools, and platforms. He holds extensive research experience with many state-of-the-art models. His research in Android security has led to some major shifts in the Android permission model.
He loves teaching and the most important reason he teaches online is to make sure that maximum people can learn through his content. Hope you have fun learning with him!</p>
        <h2>Course Outline</h2><h4>1. Intro and Setting up</h4><p></p><br><h4>2. Core Language Basics</h4><p></p><br><h4>3. Arrays and Matrices: Native Language Support</h4><p></p><br><h4>4. Functions and Fun Stuff</h4><p></p><br><h4>5. Getting Started with Data Science</h4><p></p><br><h4>6. Case Studies in Data Science</h4><p></p><br><h4>7. Deep Learning - Flux in Julia</h4><p></p><br><h4>8. Parting Words</h4><p></p>

Programming

In the fast-paced world of data science and machine learning, you have to stay up-to-date and keep ahead of the competition. For this, you have to constantly be on the lookout for the latest trends in tools and techniques for data science and machine learning. You don't want to miss out on the latest trend and the tool of the future! Right now, that tool is the Julia programming language. It's the hot new language that all ML and data science experts are very excited about. Learning Julia will open up several doors for you in your career!

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