Name: Introduction to Data Science - free for Medway residents
Availability: InStock

<div>
</div>
<section>
<div>
<h2><strong>About this Virtual Instructor Led Training (VILT)</strong></h2>
<p>The energy industry has started its journey to be more data centric by embracing the industry 4.0 concept. As a result, data management - which was considered until recently as a back-office service to support geoscience, reservoir management, engineering, production and maintenance - is now given the spotlight!</p>
<p>To become an active stakeholder in this important transition in E&amp;P data management, it is necessary to understand the new technical opportunities offered by the Cloud, Artificial Intelligence and how data governance can pave the way towards more reliable and resilient processes within E&amp;P domain.</p>
<p>Several key questions that need to be addressed:</p>
<ol>
<li>Why place more focus on data assets?</li>
<li>Is data management just about serving geoscientists or engineers with fresh data?</li>
<li>What is the value of data management in the E&amp;P sector for decision making?</li>
<li>How to convince the data consumers that the data we provide is reliable?</li>
<li>Is the data architecture of my organization appropriate and sustainable?</li>
</ol>
<p>The purpose of this 5 half-day Virtual Instructor Led Training (VILT) course is to present the data challenges facing the energy organizations today and see how they practically solve them. The backbone of this course is based on the <strong>DAMA Book of Knowledge for Data Management</strong>. The main data management activities are described in sequence with a particular focus on recent technological developments.</p>
</div>
</section>
<section>
<div id="accordion">
<div>
<div>

Training Objectives 

</div>
<div id="collapseOne">
<div>
<p>Upon completion of this VILT course, the participants will be able to:</p>
<ul>
<li>Understand why the data asset is now considered as a main asset by energy organizations</li>
<li>Appreciate the importance of data governance and become an active stakeholder of it</li>
<li>Understand the architecture and implementation of data structure in their professional environment</li>
<li>Get familiarized with the more important data management activities such as data security and data quality</li>
<li>Integrate their subsurface and surface engineering skills with the data managements concepts</li>
</ul>
<p>This VILT course is unique on several points:</p>
<ul>
<li>All notions are explained by some short presentations. For each of them, a set of video, exercises, quizzes will be provided to help develop an engaging experience between the trainer and the participants</li>
<li>A pre-course questionnaire to help the trainer focus on the participants' needs and learning objectives</li>
<li>A detailed reference manual</li>
<li>A lexicon of terms for data-management</li>
<li>Limited class size to encourage the interactivity</li>
</ul>
</div>
</div>
</div>
<div>
<div>

Target Audience 

</div>
<div id="collapseTwo">
<div>
<p>This VILT course is intended for:</p>
<ul>
<li>Junior/new data managers</li>
<li>Geoscientists</li>
<li>Reservoir engineers</li>
<li>Producers</li>
<li>Maintenance specialists</li>
<li>Construction specialists</li>
<li>Human resources</li>
<li>Legal</li>
</ul>
</div>
</div>
</div>
<div>
<div>

Course Level 

</div>
<div id="collapsecl">
<div>
<ul>
<li>Basic or Foundation</li>
</ul>
</div>
</div>
</div>
<div>
<div>

Training Methods 

</div>
<div id="collapseThree">
<div>
<p>The VILT course will be delivered online in <strong>5 half-days consisting 4 hours per day</strong>, with 2 breaks of 10 minutes per day.</p>
<p>Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total).</p>
</div>
</div>
</div>
<div>
<div>

Trainer

</div>
<div id="collapseFive">
<div>
<p>Your expert course leader is a geologist by education who has dedicated his career to subsurface data management services. In 2016, he initiated a tech startup dedicated to Data Management using Artificial Intelligence (AI) tools. He is heavily involved in developing business plans, pricing strategies, partnerships, marketing and SEO, and is the co-author of several Machine Learning publications. He also delivers training on Data Management and Data Science to students and professionals.</p>
<p>Based in France, he was formerly Vice President, Sales &amp; Marketing at CGG where he was in charge of the Data Management Services strategy, Sales Manager at Spie O&amp;G Services where he initiated the Geoscience technical assistance activities and Product Manager of interactive seismic inversion software design and marketing at Paradigm.</p>
<p> </p>
<p> </p>
<p> </p>
</div>
</div>
</div>
<div>
<div>

POST TRAINING COACHING SUPPORT (OPTIONAL)

</div>
<div id="collapseCS">
<div>
<p>To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster.<br />
Request for further information about post training coaching support and fees applicable for this.</p>
</div>
</div>
</div>
<div>
<div>

Accreditions And Affliations

</div>
<div id="collapseSeven">
<div>
               </div>
</div>
</div>
</div>
</section>

EnergyEdge - Training for a Sustainable Energy Future

Renewable Energy

Oil & Gas

Fundamentals of E&P Data Management

Team of 10, pp

Normal Fee, pp

Loughborough

<div>
		

		<div>
			
			<p>This course will enable you to bring value to the business by putting data science concepts into practice. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations.</p>
			<div><i></i> 5 Days - 1 Exam</div>
			<div><i></i>  £595.00 incl. VAT</div>
			
			
		</div>
	</div>

	

	<div id="filters">
		
		
	</div>

	

    <div>

		
		 
		

		<div></div>

		<div>
			<p>Want to run this event in-house?  Enquire about running this event in-house </p>
			
		</div>

		<div>
			<p>Interested in attending? Have a suggestion about running this event near you?<br />Register your interest now</p>
			
		</div>
	</div>
	<div></div>
	
		
        <div>
			
		</div>
				

	
		<h3>Description</h3>
		<p></p><p>For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice. This course includes hands-on activities for each topic area.</p>
<h3>Course Objectives: </h3>
<p>In this course, you will implement data science techniques in order to address business issues. You will:</p>
<p>• Use data science principles to address business issues. <br />• Apply the extract, transform, and load (ETL) process to prepare datasets. <br />• Use multiple techniques to analyze data and extract valuable insights. <br />• Design a machine learning approach to address business issues. <br />• Train, tune, and evaluate classification models. <br />• Train, tune, and evaluate regression and forecasting models. <br />• Train, tune, and evaluate clustering models. <br />• Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance.</p>
<p></p>
	
		<h3>Target Student</h3>
		<p></p><p>This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming.  However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. </p>
<p>Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business.  This course is also designed to assist students in preparing for the Certified Data Science Practitioner certification.</p>
<p></p>
	
		<h3>Prerequisites</h3>
		<p></p><p>To ensure your success in this course, you should have at least a high-level understanding of fundamental data science concepts, including, but not limited to: types of data, data science roles, the overall data science lifecycle, and the benefits and challenges of data science.</p>
<p>You should have also have experience with high-level programming languages like Python. Being comfortable using fundamental Python data science libraries like NumPy and pandas is highly recommended.</p>
<p>In addition to programming, you should also have experience working with databases, including querying languages like SQL. </p>
<p></p>
	
		<h3>Modules/Lessons</h3>
		<p></p><h4>Lesson 1: Addressing Business Issues with Data Science</h4>
<div>
<p>Topic A: Initiate a Data Science Project<br />Topic B: Formulate a Data Science Problem</p>
<h4>Lesson 2: Extracting, Transforming, and Loading Data</h4>
<p>Topic A: Extract Data <br />Topic B: Transform Data <br />Topic C: Load Data</p>
<h4>Lesson 3: Analyzing Data</h4>
<p>Topic A: Examine Data<br />Topic B: Explore the Underlying Distribution of Data <br />Topic C: Use Visualizations to Analyze Data<br />Topic D: Preprocess Data</p>
<h4>Lesson 4: Designing a Machine Learning Approach</h4>
<p>Topic A: Identify Machine Learning Concepts<br />Topic B: Test a Hypothesis</p>
</div>
<h4>Lesson 5: Developing Classification Models</h4>
<p>Topic A: Train and Tune Classification Models <br />Topic B: Evaluate Classification Models</p>
<h4>Lesson 6: Developing Regression Models</h4>
<p>Topic A: Train and Tune Regression Models <br />Topic B: Evaluate Regression Models</p>
<h4>Lesson 7: Developing Clustering Models</h4>
<p>Topic A: Train and Tune Clustering Models <br />Topic B: Evaluate Clustering Models</p>
<h4>Lesson 8: Finalizing a Data Science Project</h4>
<p>Topic A: Communicate Results to Stakeholders <br />Topic B: Demonstrate Models in a Web App <br />Topic C: Implement and Test Production Pipelines</p>
<p></p>

Mpi Learning - Professional Learning And Development Provider

This course will enable you to bring value to the business by putting data science concepts into practice. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, but it can also inform - by guiding decisions and influencing day-to-day operations.

Certified Data Science Practitioner

General Admission

Medway

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Overview</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This is an 8-hour course aimed to help you learn what is meant by Data Science, what it involves and how it is often done, then to complete a small practical project to use some of the techniques and to get an idea of whether this is something that you might want to take further.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">How you will learn</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will be coached and mentored to complete the<em fr-original-style="" style="box-sizing: border-box;"> CISCO Skills for All </em>online course<em fr-original-style="" style="box-sizing: border-box;"> "Introduction to Data Science"</em> over a series of 3 two-hour, one per week. sessions with an opportunity to continue the learning in between sessions.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Each week there will be a half-hour workshop introducing a data tool (spreadsheet, visualisation, story-telling)</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will choose a data set that interests you and, in an final 2 hour session, you will complete a small practical project with your chosen dataset and one or more of the tools introduced in previous weeks.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Topics</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will find a list of topics covered in the online course here</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Achievements</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will earn CISCO badges</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Introduction to Data Science</li><li fr-original-style="" style="box-sizing: border-box;">Basics of Analytics</li><li fr-original-style="" style="box-sizing: border-box;">Data Collection</li><li fr-original-style="" style="box-sizing: border-box;">Artificial Intelligence and Machine Learning Concepts</li><li fr-original-style="" style="box-sizing: border-box;">Data Careers</li></ul><div fr-original-style="" style="box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">and a statement of achievement from futureCodersSE CIC, with a shared link to your project to add to your CV or LinkedIn profile.</span></div><div fr-original-style="" style="box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></div>

futureCoders SE

Data Storytelling

Learn the basics of Data Science, combining a supported #CISCO Skills for All online course with practical learning and a project to help consolidate the learning.

Introduction to Data Science

General admission

<div>
		

		<div>
			
			<p>In the past, popular thought treated artificial intelligence (AI) as if it were the domain of science fiction or some far-flung future. In the last few years, however, AI has been given new life. The business world has especially given it renewed interest. However, AI is not just another technology or process for the business to consider - it is a truly disruptive force.</p>
			<div><i></i> 120 minutes</div>
			<div><i></i>  £50.00 incl. VAT</div>
			
			
		</div>
	</div>

	

	<div id="filters">
		
		
	</div>

	

    <div>

		
		 
		

		<div></div>

		<div>
			<p>Want to run this event in-house?  Enquire about running this event in-house </p>
			
		</div>

		<div>
			<p>Interested in attending? Have a suggestion about running this event near you?<br />Register your interest now</p>
			
		</div>
	</div>
	<div></div>
	
		
        <div>
			
		</div>
				

	
		<h3>Description</h3>
		<p></p><p>In the past, popular thought treated artificial intelligence (AI) as if it were the domain of science fiction or some far-flung future. In the last few years, however, AI has been given new life. The business world has especially given it renewed interest. However, AI is not just another technology or process for the business to consider-it is a truly disruptive force, one that promises to deliver an entirely new level of results for all aspects of the business. Even organizations that resist adopting AI will feel its impact. If the organization wants to thrive and survive in this transforming business landscape, it will need to harness the power of AI.</p>
<p>Despite its promises, AI can seem like a daunting concept for business professionals. How can you hope to apply AI to your own business if you can't see beyond the vague buzzwords and hype? That's why this course was created: to give you the essential knowledge of AI you'll need to steer the business forward.</p>
<p></p>
	
		<h3>Target Student</h3>
		<p></p><p>This course is primarily designed for managers, business leaders, and other decision-makers who are interested in growing the business by leveraging the power of AI. Other individuals who wish to explore basic AI concepts are also candidates for this course.</p>
<p></p>
	
		<h3>Prerequisites</h3>
		<p></p><p>To ensure your success in this course, you should have foundational knowledge of business processes and general concepts. You should also have at least a basic understanding of information technology resources and systems, including networks, computers, and other electronic devices used in the enterprise.</p>
<p></p>
	
		<h3>Modules/Lessons</h3>
		<p></p><h4><span><strong>Lesson 1: AI Fundamentals</strong></span></h4>
<p>Topic A: AI Concepts<br />Topic B: Approaches to Machine Learning and Deep Learning</p>
<h4><span><strong>Lesson 2: AI Implementations</strong></span></h4>
<p>Topic A: Data Science<br />Topic B: Search Engines</p>
<h4><span><strong>Lesson 2: AI Implementations (cont.)</strong></span></h4>
<p>Topic C: Natural Language Processing<br />Topic D: Computer Vision<br />Topic E: Robotics</p>
<h4><span><strong>Lesson 4: The Impact of AI</strong></span></h4>
<p>Topic A: Benefits of AI<br />Topic B: Challenges of AI<br />Topic C: Business Use Cases for AI</p>
<p></p>

Online Course Marketing

Business Analytics & Intelligence

Generative AI

Business Information Technology

Microsoft AI-900

AI (Artificial Intelligence)

In the past, popular thought treated artificial intelligence (AI) as if it were the domain of science fiction or some far-flung future. In the last few years, however, AI has been given new life. The business world has especially given it renewed interest. However, AI is not just another technology or process for the business to consider - it is a truly disruptive force.

AI For Leaders

<div>
		

		<div>
			
			<p>Data Ethics for Business Professionals is designed for individuals who are seeking to demonstrate an understanding of the ethical uses of data in business settings.</p>
			<div><i></i> 6 hours or 3 x 90 min sessions</div>
			<div><i></i>  £45.00 incl. VAT</div>
			
			
		</div>
	</div>

	

	<div id="filters">
		
		
	</div>

	

    <div>

		
		 
		

		<div></div>

		<div>
			<p>Want to run this event in-house?  Enquire about running this event in-house </p>
			
		</div>

		<div>
			<p>Interested in attending? Have a suggestion about running this event near you?<br />Register your interest now</p>
			
		</div>
	</div>
	<div></div>
	
		
        <div>
			
		</div>
				

	
		<h3>Description</h3>
		<p></p><p><span>This course empowers professionals to identify sources of ethical risk and move beyond transparency to understand all ethical principles while balancing business considerations of ethically driven technology.</span></p>
<p></p>
	
		<h3>Modules/Lessons</h3>
		<p></p><h4>Overview</h4>
<p>The power of extracting value from data utilizing Artificial Intelligence, Data Science and Machine Learning exposes the learning differences between humans and machines. Humans can apply ethical principles throughout the decision-making process to avoid discrimination, societal harm, and marginalization to maintain and even enhance acceptable norms. Machines make decisions autonomously. So how do we apply ethical principles to data driven technology? This course provides business professionals and consumers of technology core concepts of ethical principles, how they can be applied to emerging data driven technologies and the impact to an organization which ignores ethical use of technology.</p>
<h4>Course Length:</h4>
<p>6 hours instructional time Content is designed to be presented as a full day class, or in three 90-minute sessions by lesson.</p>
<h4>Course Content:</h4>
<p>Each lesson includes activities, indicated in orange and may include resources for learners to integrate ethical practices into their own organization, indicated in blue.</p>
<h4>Introduction to Data Ethics</h4>
<p>Define Ethics <br />Define Data <br />Define Data Ethics <br />Principles of Data Ethics <br />The Case for Data Ethics<br />Identifying Ethical Issues</p>
<h4>Ethical Principles</h4>
<p>Ethical Frameworks <br />Applying Ethical Frameworks <br />Privacy, Fairness, and Safety <br />Applying Privacy, Fairness, and Safety Principles <br />Algorithms and Human-Centered Values <br />Discussing True and False Positives and Negatives <br />Discussing Accuracy and Precision <br />Discussing Correlation and Causation <br />Transparency and Explainability: The Black Box Problem <br />Discussing Black Box Parallels <br />Inclusive Growth, Sustainable Development, and Well-Being <br />Examining a Tech for Good Organization <br />Improving Ethical Data Practices</p>
<h4>Sources of Ethical Risk</h4>
<p>Bias and Discrimination <br />Case Study: Allegheny Family Screening Tool <br />Data Surveillance <br />Safety and Security <br />Case Study: PredPol</p>
<h4>Business Considerations</h4>
<p>Data Legislation <br />Manage the Effects of Data <br />Case Study <br />Embed Organizational Values in the Data Value Chain <br />Building a Data Ethics Culture/Code of Ethics</p>
<h4>Exam</h4>
<p><span>A Data Ethics Business Professional is an individual who has demonstrated foundational knowledge of the ethical use of data in emerging technologies in a business. A credential holder is able to identify, explain, and document how organizations identify ethical principles and considerations, understand sources of risk, and explain how businesses can create sustainable, ethical decision-making processes.</span></p>
<p><span>Exam Details: 40 Mins, Multiple Choice Online Assessment. </span></p>
<p></p>
	
		<h3>Target Student</h3>
		<p></p><p>This course is designed for business leaders and decision makers, including C-level executives, project and product managers, HR leaders, Marketing and Sales leaders, and technical sales consultants, who have a vested interest in the representation of ethical values in technology solutions. Other individuals who want to know more about data ethics are also candidates for this course.</p>
<p></p>
	
		<h3>Prerequisites</h3>
		<p></p><p>To ensure your success in this course, you should have a working knowledge of general business concepts and practices. You should also have a basic understanding of Artificial Intelligence and or Data Science.</p>
<p></p>

Data Ethics for Business Professionals is designed for individuals who are seeking to demonstrate an understanding of the ethical uses of data in business settings.

Data Ethics for Business Professionals

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Overview</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This is an 8-hour course aimed to help you learn what is meant by Data Science, what it involves and how it is often done, then to complete a small practical project to use some of the techniques and to get an idea of whether this is something that you might want to take further.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">How you will learn</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will be coached and mentored to complete the<em fr-original-style="" style="box-sizing: border-box;"> CISCO Skills for All </em>online course<em fr-original-style="" style="box-sizing: border-box;"> "Introduction to Data Science"</em> over a series of 3 two-hour, one per week. sessions with an opportunity to continue the learning in between sessions.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Each week there will be an hour long workshop introducing a data tool (spreadsheet, visualisation, story-telling) and giving you practical exercises to do.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will choose a data set that interests you and, in an final 2 hour session, you will complete a small practical project with your chosen dataset and one or more of the tools introduced in previous weeks.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Topics</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will find a list of topics covered in the online course here</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Achievements</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You will earn CISCO badges</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Introduction to Data Science</li><li fr-original-style="" style="box-sizing: border-box;">Basics of Analytics</li><li fr-original-style="" style="box-sizing: border-box;">Data Collection</li><li fr-original-style="" style="box-sizing: border-box;">Artificial Intelligence and Machine Learning Concepts</li><li fr-original-style="" style="box-sizing: border-box;">Data Careers</li></ul><div fr-original-style="" style="box-sizing: border-box;"><span style="font-size: 16px; box-sizing: border-box;" fr-original-style="font-size: 16px;">and a statement of achievement from futureCodersSE CIC, with a shared link to your project to add to your CV or LinkedIn profile.</span></div><div fr-original-style="" style="box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;"></div>

Introduction to Data Science - free for Medway residents

Commercial & Contract Management Certification Program (CCM)

Emotional Literacy

Life Coach Training

Azure Machine Learning

Learning & Development

Budgeting

Trainers Course

Psychology Counselling

Emotional Wellbeing

Master Your Emotions

Resume & CV Writing

Global Leadership

training consultancy

Financial Modelling & Analysis

Finance For Non Finance Managers

Institute of Leadership & Management (ILM)

Machine Learning (ML)

business training

Business Leadership

Emotional Intelligence (EQ)

Psychological Wellbeing Practitioner

Inclusive Recruitment

Commercial Manager

Diversity & Inclusion

Continuous Professional Development (CPD)

bespoke learning

Unconscious Bias

business learning and development

Professional Skills Course (PSC)

Recruitment Consultant

ACT Awareness

NVQ Assessor

Counselling Skills

Leadership Development

Dementia Training

Mental Health At Work

Equity & Diversity

Lean Awareness

Leadership Skills

Project Leadership

Manager Training

Neurodiversity

Women In Leadership

NLP Practitioner

Professional Development

Autism Spectrum Counselling & Training (ASPECT)

Equality & Diversity

Corporate Finance Level 1

Business Analysis

Disability Awareness

Policy Writing

<strong>This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.</strong>
        <p>Instead of using hard-coded rules for performing something, artificial intelligence (AI) and machine learning (ML) prepare the machines to learn things from data to decipher the complex patterns automatically and then use it for building multiple use cases. If you too want to make applications and solve complex problems using AI and ML, then this course will help you to get started with the basics.

The course starts by highlighting the learning objectives and explains the rise of AI in today's world. Next, you will be introduced to the concepts of ML. You will learn to understand ML terminologies and learn how to train a model. Towards the end, you will become familiar with the classification of ML systems, including the topics of supervised and unsupervised learning, regression, clustering, and reinforcement.

By the end of this course, you will have a fundamental knowledge of AI and ML.

All the resource for this course are available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>What You Will Learn</h2><p>Find out why machine learning (ML) is called a black box<br>Get to grips with the features and labels of ML<br>Uncover techniques for training ML models<br>Find out the differences between supervised and unsupervised learning<br>Understand regression, clustering, and dimension reduction</p>
        <h2>Audience</h2><p>If you are a developer, product or project manager, engineer, or a data science enthusiast, who is looking to learn the fundamentals of artificial intelligence (AI) and machine learning (ML), this course is for you. There is no prerequisite to start this course as it is specially designed for absolute beginners.</p>
        <h2>Approach</h2><p>With the help of simple narration, interesting examples, and practical tips, this course helps you to get started with artificial intelligence (AI) and machine learning (ML).</p>
        <h2>Key Features</h2><p>Learn the basics of artificial intelligence (AI), machine learning (ML), and deep learning * Find out how ML helps in training a model * Discover how ML systems are classified</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners---Level-1</p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

Idan's expertise spans multiple domains, including cloud computing, machine learning, data science, and electronics. Since 2014, Idan has created and published online courses on various topics worldwide. Idan is recognized as a high-rated instructor by leading educational providers.

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 1!</h4><p></p><br><h4>2. The Rise of Artificial Intelligence</h4><p></p><br><h4>3. Introduction to Machine Learning</h4><p></p><br><h4>4. Classification of ML Systems</h4><p></p><br><h4>5. Course Summary</h4><p></p>

Packt

Deep Learning

This course will take you through the fundamental concepts of machine learning (ML) and artificial intelligence (AI). By the end of this course, you will be ready to dive into the advanced concepts of ML.

Machine Learning for Absolute Beginners - Level 1

<strong>This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.</strong>
        <p>From self-driving cars to artificial intelligence (AI) bots, machine learning (ML) is slowly spreading its reach and making our devices smarter. If you have ever wanted to play a role in the future of technology development, then here is your chance to get started with ML. This course breaks the complex topics of ML into simple concepts that are easier to understand.
The course starts with an introduction to ML, explaining its applications in the real-world and how it is different from AI. Next, you will learn supervised and unsupervised algorithms and understand the role of neural networks in ML. Once you understand the ML algorithms, you will dive into building interesting projects to consolidate your learning. You will learn how to build a board game review prediction model, how to build a credit card fraud detection model, how to tokenize word and sentences using natural language processing), how to build an object recognition model, how to build an image quality improvement model, how to build a text classification model, how to build an image analysis model, and how to build a data compression model.
By the end of this course, you will have gained the skills to create real-world ML solutions.
All the recourses for this course are avialable at https://github.com/PacktPublishing/Projects-in-Machine-Learning-From-Beginner-to-Professional</p>
        <h2>What You Will Learn</h2><p>Detect credit card fraud by using probability densities<br>Become familiar with the natural language processing methodology<br>Use the Canadian Institute for Advanced Research-10 (CIFAR-10) object recognition dataset to implement a deep neural network<br>Improve image quality using Super-Resolution Convolutional Neural Network (SRCNN)<br>Solve a text classification task using multiple classification algorithms<br>Use K-means clustering in an unsupervised algorithm</p>
        <h2>Audience</h2><p>If you want to understand machine learning (ML) algorithms and concepts to build effective ML solutions for the modern world, this course is for you. Basic Python skills and a good understanding of mathematics are needed to get started with this course.</p>
        <h2>Approach</h2><p>With the help of engaging project building activities, examples, and quizzes, this course helps you to master the concepts of machine learning and gives you the confidence to build real-world solutions using supervised and unsupervised algorithms, neural networks, and a lot more.</p>
        <h2>Key Features</h2><p>Grasp the core concepts of machine learning (ML) * Find out how to use neural networks in ML projects * Learn how to build real-world projects using supervised and unsupervised learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Projects-in-Machine-Learning-From-Beginner-to-Professional</p>
        <h2>About the Author</h2><h3>Eduonix Learning Solutions</h3><p>Eduonix learning Solutions is a premier training and skill development organization which was started with a vision to bring world class training content, pedagogy and best learning practices to everyone's doorsteps . Eduonix aims to identify and provide the best learning and training environment. It identifies industry veterans and content creators around the globe and bring it to the global audience using number of intuitive platforms for easy and affordable access to quality content. Eduonix offers easy to understand online courses and workshops for everyday people. If you have ever wanted to learn a new skill, but don't want to attend four years of college to do it, we have a solution for you.</p>
        <h2>Course Outline</h2><h4>1. Introduction to Machine Learning (ML)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to this course.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Machine Learning (ML)?</p><p style='margin: 0'>This video explains the concept of ML.</p></td></tr><tr><td style='padding: 15px;'><p>3. Types and Applications of Machine Learning (ML)</p><p style='margin: 0'>This video explains the various types and applications of ML.</p></td></tr><tr><td style='padding: 15px;'><p>4. Artificial Intelligence (AI) versus Machine Learning (ML)</p><p style='margin: 0'>This video illustrates the difference between AI and ML.</p></td></tr><tr><td style='padding: 15px;'><p>5. Essential Mathematics for Machine Learning (ML) and Artificial Intelligence (AI)</p><p style='margin: 0'>This video highlights some mathematical concepts that are needed to understand ML and AI.</p></td></tr></tbody></table><br><h4>2. Supervised Learning - Part 1</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Supervised Learning</p><p style='margin: 0'>This video provides an introduction to supervised learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Linear Methods for Classification</p><p style='margin: 0'>This video explains the liner methods for classification.</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear Methods for Regression</p><p style='margin: 0'>This video explains linear methods for regression.</p></td></tr><tr><td style='padding: 15px;'><p>4. Support Vector Machines (SVM)</p><p style='margin: 0'>This video explains the concept of SVM.</p></td></tr><tr><td style='padding: 15px;'><p>5. Basic Expansions</p><p style='margin: 0'>This video explains some basic expansions.</p></td></tr><tr><td style='padding: 15px;'><p>6. Model Selection Procedures</p><p style='margin: 0'>This video focuses on model selection procedures.</p></td></tr><tr><td style='padding: 15px;'><p>7. Bonus! Supervised Learning Project in Python - Part 1</p><p style='margin: 0'>This is the first part of a two-part video that presents a supervised learning project in Python.</p></td></tr><tr><td style='padding: 15px;'><p>8. Bonus! Supervised Learning Project in Python - Part 2</p><p style='margin: 0'>This is the second part of a two-part video that presents a supervised learning project in Python.</p></td></tr></tbody></table><br><h4>3. Unsupervised Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Unsupervised Learning</p><p style='margin: 0'>This video provides an introduction to unsupervised learning.</p></td></tr><tr><td style='padding: 15px;'><p>2. Association Rules</p><p style='margin: 0'>This video focuses on the association rule learning method.</p></td></tr><tr><td style='padding: 15px;'><p>3. Cluster Analysis</p><p style='margin: 0'>This video explains the concept of cluster analysis.</p></td></tr><tr><td style='padding: 15px;'><p>4. Reinforcement Learning</p><p style='margin: 0'>This video explains the concept of reinforcement learning.</p></td></tr><tr><td style='padding: 15px;'><p>5. Bonus! K-Means Clustering Project</p><p style='margin: 0'>This video presents a project on K-means clustering.</p></td></tr></tbody></table><br><h4>4. Neural Networks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Neural Networks</p><p style='margin: 0'>This video provides an introduction to neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>2. Perceptron</p><p style='margin: 0'>This video explains the concept of perceptron in neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. Backpropagation Algorithm</p><p style='margin: 0'>This video explains the backpropagation algorithm in neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>4. Training Procedures</p><p style='margin: 0'>This video focuses on training procedures in neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>5. Convolutional Neural Network (CNN)</p><p style='margin: 0'>This video explains the concept of CNN.</p></td></tr></tbody></table><br><h4>5. Real-world Machine Learning (ML)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to Real-world Machine Learning (ML)</p><p style='margin: 0'>This video provides an introduction to real-world ML.</p></td></tr><tr><td style='padding: 15px;'><p>2. Choosing an Algorithm</p><p style='margin: 0'>This video explains how to choose an algorithm.</p></td></tr><tr><td style='padding: 15px;'><p>3. Design and Analysis of Machine Learning (ML) Experiments</p><p style='margin: 0'>This video focuses on the design and analysis of ML experiments.</p></td></tr><tr><td style='padding: 15px;'><p>4. Common Software for Machine Learning (ML)</p><p style='margin: 0'>This video highlights some common software needed for ML.</p></td></tr></tbody></table><br><h4>6. Final Project</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Setting up OpenAI Gym</p><p style='margin: 0'>This video explains how to set up OpenAI gym.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building and Training the Network - Part 1</p><p style='margin: 0'>This is the first part of a two-part video that demonstrates how to build and train the network.</p></td></tr><tr><td style='padding: 15px;'><p>3. Building and Training the Network - Part 2</p><p style='margin: 0'>This is the second part of a two-part video that demonstrates how to build and train the network.</p></td></tr></tbody></table><br><h4>7. Project 1 - Board Game Review Prediction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Building the Dataset - Part 1</p><p style='margin: 0'>This is the first part of a two-part video that explains how to build the dataset for the project.</p></td></tr><tr><td style='padding: 15px;'><p>3. Building the Dataset - Part 2</p><p style='margin: 0'>This is the second part of the two-part video that explains how to build the dataset for the project.</p></td></tr><tr><td style='padding: 15px;'><p>4. Training Models</p><p style='margin: 0'>This video demonstrates how to train models.</p></td></tr></tbody></table><br><h4>8. Project 2 - Credit Card Fraud Detection t</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Credit Card Fraud Detection - Dataset</p><p style='margin: 0'>This video explains how to build a dataset for the project.</p></td></tr><tr><td style='padding: 15px;'><p>3. Credit Card Fraud Detection - Algorithms</p><p style='margin: 0'>This video demonstrates how to work on algorithms for the project.</p></td></tr></tbody></table><br><h4>9. Project 3 - Getting Started with Natural Language Processing (NLP) in Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provided an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Tokenizing, Stopwords, and Stemming</p><p style='margin: 0'>This video explains the concepts of tokenizing, stopwords, and stemming.</p></td></tr><tr><td style='padding: 15px;'><p>3. Tagging, Chunking, and Named Entity Recognition</p><p style='margin: 0'>This video explains the concepts of tagging, chunking, and named entity recognition.</p></td></tr><tr><td style='padding: 15px;'><p>4. Text Classification</p><p style='margin: 0'>This video explains the process of text classification.</p></td></tr></tbody></table><br><h4>10. Project 4 - Obtaining Near State-of-the-art Performance on Object Recognition Tasks Using Deep Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Loading and Preprocessing the Canadian Institute For Advanced Research - 10 (CIFAR-10) Dataset</p><p style='margin: 0'>This video demonstrates how to load and preprocess the CIFAR-10 dataset.</p></td></tr><tr><td style='padding: 15px;'><p>3. Building and Deploying the All-Convolutional Neural Network (CNN) Network - Part 1</p><p style='margin: 0'>This is the first part of a two-part video that demonstrates how to build and deploy the All-CNN network.</p></td></tr><tr><td style='padding: 15px;'><p>4. Building and Deploying the All- Convolutional Neural Network (CNN) Network - Part 2</p><p style='margin: 0'>This is the second part of a two-part video that demonstrates how to build and deploy the All-CNN network.</p></td></tr></tbody></table><br><h4>11. Project 5 - Image Super-resolution with the Super-Resolution Convolution Neural Network (SRCNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Quality Metrics and Preprocessing Images</p><p style='margin: 0'>This video focuses on quality metrics and preprocessing images.</p></td></tr><tr><td style='padding: 15px;'><p>3. Image Super-resolution Using Deep Learning</p><p style='margin: 0'>This video explains how to perform image super-resolution using deep learning.</p></td></tr></tbody></table><br><h4>12. Project 6 - Natural Language Processing (NLP): Text Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Feature Engineering</p><p style='margin: 0'>This video explains the concept of feature engineering in Natural Language Processing.</p></td></tr><tr><td style='padding: 15px;'><p>3. Deploying Scikit-learn (Sklearn) Classifiers</p><p style='margin: 0'>This video demonstrates how to deploy sklearn classifiers.</p></td></tr></tbody></table><br><h4>13. Project 7 - K-means Clustering for Image Analysis</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Preprocessing Images for Clustering</p><p style='margin: 0'>This video demonstrates how to preprocess images for clustering.</p></td></tr><tr><td style='padding: 15px;'><p>3. Evaluation and Visualization</p><p style='margin: 0'>This video explains the concept of evaluation and visualization.</p></td></tr></tbody></table><br><h4>14. Project 8 - Data Compression and Visualization Using Principal Component Analysis (PCA)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to the project.</p></td></tr><tr><td style='padding: 15px;'><p>2. Elbow Method</p><p style='margin: 0'>This video focuses on the elbow method.</p></td></tr><tr><td style='padding: 15px;'><p>3. Principal Component Analysis (PCA) Compression and Visualization</p><p style='margin: 0'>This video explains how to perform compression and visualization using PCA.</p></td></tr></tbody></table>

Python

TensorFlow

This course covers the basic concepts of machine learning (ML) that are crucial for getting started on the journey of becoming a skilled ML developer. You will become familiar with different algorithms and networks, such as supervised, unsupervised, neural networks, Convolutional Neural Network (CNN), and Super-Resolution Convolutional Neural Network (SRCNN), needed to develop effective ML solutions.

Projects in Machine Learning: From Beginner to Professional

<div>
		

		<div>
			
			<p>This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.</p>
			<div><i></i> 5 Days 1 Exam</div>
			<div><i></i>  £595.00 incl. VAT</div>
			
			
		</div>
	</div>

	

	<div id="filters">
		
		
	</div>

	

    <div>

		
		 
		

		<div></div>

		<div>
			<p>Want to run this event in-house?  Enquire about running this event in-house </p>
			
		</div>

		<div>
			<p>Interested in attending? Have a suggestion about running this event near you?<br />Register your interest now</p>
			
		</div>
	</div>
	<div></div>
	
		
        <div>
			
		</div>
				

	
		<h3>Description</h3>
		<p></p><h3>Overview:</h3>
<p>Artificial intelligence (AI) and machine learning (ML) have become an essential part of the toolset for many organisations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organisations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.</p>
<h3>Course Objectives:</h3>
<p>In this course, you will implement AI techniques in order to solve business problems. You will:</p>
<p>• Specify a general approach to solve a given business problem that uses applied AI and ML.<br />• Collect and refine a dataset to prepare it for training and testing. <br />• Train and tune a machine learning model. <br />• Finalize a machine learning model and present the results to the appropriate audience. <br />• Build linear regression models. <br />• Build classification models. <br />• Build clustering models. <br />• Build decision trees and random forests. <br />• Build support-vector machines (SVMs). <br />• Build artificial neural networks (ANNs). <br />• Promote data privacy and ethical practices within AI and ML projects. </p>
<p> </p>
<p></p>
	
		<h3>Modules/Lessons</h3>
		<p></p><h4><span>Lesson 1: Solving Business Problems Using AI and ML</span></h4>
<p>Topic A: Identify AI and ML Solutions for Business Problems<br />Topic B: Formulate a Machine Learning Problem<br />Topic C: Select Appropriate Tools  </p>
<h4>Lesson 2: Collecting and Refining the Dataset</h4>
<p>Topic A: Collect the Dataset<br />Topic B: Analyze the Dataset to Gain Insights<br />Topic C: Use Visualizations to Analyze Data<br />Topic D: Prepare Data</p>
<h4>Lesson 3: Setting Up and Training a Model</h4>
<p>Topic A: Set Up a Machine Learning Model<br />Topic B: Train the Model </p>
<h4>Lesson 4: Finalizing a Model </h4>
<p>Topic A: Translate Results into Business Actions<br />Topic B: Incorporate a Model into a Long-Term Business Solution </p>
<h4>Lesson 5: Building Linear Regression Models</h4>
<p>Topic A: Build a Regression Model Using Linear Algebra<br />Topic B: Build a Regularized Regression Model Using Linear Algebra<br />Topic C: Build an Iterative Linear Regression Model </p>
<h4>Lesson 6: Building Classification Models</h4>
<p>Topic A: Train Binary Classification Models<br />Topic B: Train Multi-Class Classification Models<br />Topic C: Evaluate Classification Models<br />Topic D: Tune Classification Models</p>
<h4>Lesson 7: Building Clustering Models</h4>
<p>Topic A: Build k-Means Clustering Models<br />Topic B:  Build  Hierarchical  Clustering  Mode</p>
<h4>Lesson 8: Building Advanced Models</h4>
<p>Topic A: Build  Decision Tree Models<br />Topic B: Build Random Forest Model Lesson</p>
<h4>Lesson  9: Building Supports Vector Machines</h4>
<p>Topic A: Build SVM Models for Classification<br />Topic B:  Build SVM Models for Regression</p>
<h4>Lesson 10: Building Artificial Neural Networks</h4>
<p>Topic A: Build Multilayer Perceptrons (MLP)<br />Topic B: Build Convolutional Neural Networks (CNN)</p>
<h4>Lesson 11: Promoting Data Privacy and Ethical Practices</h4>
<p>Topic A: Protect Data Privacy<br />Topic B: Promote Ethical  Practices<br />Topic C: Establish Data Privacy and Ethics Policies</p>
<p></p>
	
		<h3>Prerequisites</h3>
		<p></p><p>To ensure your success in this course, you should have at least a high-level understanding of fundamental AI concepts, including, but not limited to: machine learning, supervised learning, unsupervised learning, artificial neural networks, computer vision, and natural language processing.</p>
<p>You should also have experience working with databases and a high-level programming language such as Python, Java, or C/C++.</p>
<p></p>
	
		<h3>Target Student</h3>
		<p></p><p>The skills covered in this course converge on three areas-software development, applied math and statistics, and business analysis. Target students for this course may be strong in one or two or these of these areas and looking to round out their skills in the other areas so they can apply artificial intelligence (AI) systems, particularly machine learning models, to business problems.  So the target student may be a programmer looking to develop additional skills to apply machine learning algorithms to business problems, or a data analyst who already has strong skills in applying math and statistics to business problems, but is looking to develop technology skills related to machine learning.   A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. </p>
<p>This course is also designed to assist students in preparing for the Certified Artificial Intelligence (AI) Practitioner certification.</p>
<p></p>

This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, follow a methodical workflow to develop sound solutions, use open-source, off-the-shelf tools to develop, test, and deploy those solutions, and ensure that they protect the privacy of users. This course includes hands-on activities for each topic area.

Certified Artificial Intelligence Practitioner

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>The skills covered in this course converge on four areas-software development, IT operations, applied math and statistics, and business analysis. Target students for this course should be looking to build upon their knowledge of the data science process so that they can apply AI systems, particularly machine learning models, to business problems. So, the target student is likely a data science practitioner, software developer, or business analyst looking to expand their knowledge of machine learning algorithms and how they can help create intelligent decisionmaking products that bring value to the business. A typical student in this course should have several years of experience with computing technology, including some aptitude in computer programming. This course is also designed to assist students in preparing for the CertNexus Certified Artificial Intelligence (AI) Practitioner (Exam AIP-210) certification</p>Overview<p>In this course, you will develop AI solutions for business problems.<br />You will:<br />Solve a given business problem using AI and ML.<br />Prepare data for use in machine learning.<br />Train, evaluate, and tune a machine learning model.<br />Build linear regression models.<br />Build forecasting models.<br />Build classification models using logistic regression and k -nearest neighbor.<br />Build clustering models.<br />Build classification and regression models using decision trees and random forests.<br />Build classification and regression models using support-vector machines (SVMs).<br />Build artificial neural networks for deep learning.<br />Put machine learning models into operation using automated processes.<br />Maintain machine learning pipelines and models while they are in production</p><p>Artificial intelligence (AI) and machine learning (ML) have become essential parts of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services. This course shows you how to apply various approaches and algorithms to solve business problems through AI and ML, all while following a methodical workflow for developing data-driven solutions.</p></div><div id="course-pills-2"><h4>Solving Business Problems Using AI and ML</h4><ul><li>Topic A: Identify AI and ML Solutions for Business Problems</li><li>Topic B: Formulate a Machine Learning Problem</li><li>Topic C: Select Approaches to Machine Learning</li></ul><h4>Preparing Data</h4><ul><li>Topic A: Collect Data</li><li>Topic B: Transform Data</li><li>Topic C: Engineer Features</li><li>Topic D: Work with Unstructured Data</li></ul><h4>Training, Evaluating, and Tuning a Machine Learning Model</h4><ul><li>Topic A: Train a Machine Learning Model</li><li>Topic B: Evaluate and Tune a Machine Learning Model</li></ul><h4>Building Linear Regression Models</h4><ul><li>Topic A: Build Regression Models Using Linear Algebra</li><li>Topic B: Build Regularized Linear Regression Models</li><li>Topic C: Build Iterative Linear Regression Models</li></ul><h4> Building Forecasting Models</h4><ul><li>Topic A: Build Univariate Time Series Models</li><li>Topic B: Build Multivariate Time Series Models</li></ul><h4>Building Classification Models Using Logistic Regression and k-Nearest Neighbor</h4><ul><li>Topic A: Train Binary Classification Models Using Logistic Regression</li><li>Topic B: Train Binary Classification Models Using k-Nearest Neighbor</li><li>Topic C: Train Multi-Class Classification Models</li><li>Topic D: Evaluate Classification Models</li><li>Topic E: Tune Classification Models</li></ul><h4>Building Clustering Models</h4><ul><li>Topic A: Build k-Means Clustering Models</li><li>Topic B: Build Hierarchical Clustering Models</li></ul><h4>Building Decision Trees and Random Forests</h4><ul><li>Topic A: Build Decision Tree Models</li><li>Topic B: Build Random Forest Models</li></ul><h4>Building Support-Vector Machines</h4><ul><li>Topic A: Build SVM Models for Classification</li><li>Topic B: Build SVM Models for Regression</li></ul><h4>Building Artificial Neural Networks</h4><ul><li>Topic A: Build Multi-Layer Perceptrons (MLP)</li><li>Topic B: Build Convolutional Neural Networks (CNN)</li><li>Topic C: Build Recurrent Neural Networks (RNN)</li></ul><h4>Operationalizing Machine Learning Models</h4><ul><li>Topic A: Deploy Machine Learning Models</li><li>Topic B: Automate the Machine Learning Process with MLOps</li><li>Topic C: Integrate Models into Machine Learning Systems</li></ul><h4>Maintaining Machine Learning Operations</h4><ul><li>Topic A: Secure Machine Learning Pipelines</li><li>Topic B: Maintain Models in Production</li></ul></div>

Nexus Human

AWS Certified Solutions Architect

CertNexus Certified Artificial Intelligence Practitioner CAIP (AIP-210)

<strong>Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!</strong>
        <p>Do you ever want to be a data scientist and build Machine Learning projects that can solve real-life problems? If yes, then this course is perfect for you.
You will train machine learning algorithms to classify flowers, predict house price, identify handwritings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more!
Inside the course, you'll learn how to:
&bull;	Set up a Python development environment correctly
&bull;	Gain complete machine learning toolsets to tackle most real-world problems
&bull;	Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
&bull;	Combine multiple models with by bagging, boosting or stacking
&bull;	Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
&bull;	Develop in Jupyter (IPython) notebook, Spyder and various IDE
&bull;	Communicate visually and effectively with Matplotlib and Seaborn
&bull;	Engineer new features to improve algorithm predictions
&bull;	Make use of train/test, K-fold and Stratified K-fold cross-validation to select the correct model and predict model perform with unseen data
&bull;	Use SVM for handwriting recognition, and classification problems in general
&bull;	Use decision trees to predict staff attrition
&bull;	Apply the association rule to retail shopping datasets
&bull;	And much more!
By the end of this course, you will have a Portfolio of 12 Machine Learning projects that will help you land your dream job or enable you to solve real-life problems in your business, job or personal life with Machine Learning algorithms.</p>
        <h2>What You Will Learn</h2><p>&bull;&nbsp;&nbsp;Learn to Build Powerful Machine Learning Models to Solve Any Problem<br>&bull;&nbsp;&nbsp;Learn to Train machine learning algorithms to predict house prices, identify handwriting, detect cancer cells &amp; more</p>
        <h2>Audience</h2><p>A newbie who wants to learn machine learning algorithm with Python. Anyone who has a deep interest in the practical application of machine learning to real world problems. Anyone wishes to move beyond the basics and develop an understanding of the whole range of machine learning algorithms. Any intermediate to advanced EXCEL users who is unable to work with large datasets. Anyone interested to present their findings in a professional and convincing manner. Anyone who wishes to start or transit into a career as a data scientist. Anyone who wants to apply machine learning to their domain.</p>
        <h2>Approach</h2><p>You'll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.</p>
        <h2>Key Features</h2><p>&bull;&nbsp;&nbsp;Solve any problem in your business or job with powerful Machine Learning models * &bull;&nbsp;&nbsp;Go from zero to hero in Python, Seaborn, Matplotlib, Scikit-Learn, SVM, and unsupervised Machine Learning etc.</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/the-complete-machine-learning-course-with-python</p>
        <h2>About the Author</h2><h3>Anthony Ng</h3><p>Anthony Ng has spent almost 10 years in the education sector covering topics such as algorithmic trading, financial data analytics, investment, and portfolio management and more. He has worked in various financial institutions and has assisted Quantopian to conduct Algorithmic Trading Workshops in Singapore since 2016. He has also presented in QuantCon Singapore 2016 and 2017. He is passionate about finance, data science and Python and enjoys researching, teaching and sharing knowledge. He holds a Master of Science in Financial Engineering from NUS Singapore and MBA and Bcom from Otago University.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What Does the Course Cover?</p><p style='margin: 0'>Introduction: What Does the Course Cover?</p></td></tr></tbody></table><br><h4>2. Getting Started with Anaconda</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. [Windows OS] Downloading & Installing Anaconda</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Downloading &amp; Installing Anaconda</p></td></tr><tr><td style='padding: 15px;'><p>2. [Windows OS] Managing Environment</p><p style='margin: 0'>Getting Started with Anaconda: [Windows OS] Managing Environment</p></td></tr><tr><td style='padding: 15px;'><p>3. Navigating the Spyder & Jupyter Notebook Interface</p><p style='margin: 0'>Getting Started with Anaconda: Navigating the Spyder &amp; Jupyter Notebook Interface</p></td></tr><tr><td style='padding: 15px;'><p>4. Downloading the IRIS Datasets</p><p style='margin: 0'>Getting Started with Anaconda: Downloading the IRIS Datasets</p></td></tr><tr><td style='padding: 15px;'><p>5. Data Exploration and Analysis</p><p style='margin: 0'>Getting Started with Anaconda: Data Exploration and Analysis</p></td></tr><tr><td style='padding: 15px;'><p>6. Presenting Your Data</p><p style='margin: 0'>Getting Started with Anaconda: Presenting Your Data</p></td></tr></tbody></table><br><h4>3. Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Regression: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Categories of Machine Learning</p><p style='margin: 0'>Regression: Categories of Machine Learning</p></td></tr><tr><td style='padding: 15px;'><p>3. Working with Scikit-Learn</p><p style='margin: 0'>Regression: Working with Scikit-Learn</p></td></tr><tr><td style='padding: 15px;'><p>4. Boston Housing Data - EDA</p><p style='margin: 0'>Regression: Boston Housing Data - EDA</p></td></tr><tr><td style='padding: 15px;'><p>5. Correlation Analysis and Feature Selection</p><p style='margin: 0'>Regression: Correlation Analysis and Feature Selection</p></td></tr><tr><td style='padding: 15px;'><p>6. Simple Linear Regression Modelling with Boston Housing Data</p><p style='margin: 0'>Regression: Simple Linear Regression Modelling with Boston Housing Data</p></td></tr><tr><td style='padding: 15px;'><p>7. Robust Regression</p><p style='margin: 0'>Regression: Robust Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Evaluate Model Performance</p><p style='margin: 0'>Regression: Evaluate Model Performance</p></td></tr><tr><td style='padding: 15px;'><p>9. Multiple Regression with statsmodel</p><p style='margin: 0'>Regression: Multiple Regression with statsmodel</p></td></tr><tr><td style='padding: 15px;'><p>10. Multiple Regression and Feature Importance</p><p style='margin: 0'>Regression: Multiple Regression and Feature Importance</p></td></tr><tr><td style='padding: 15px;'><p>11. Ordinary Least Square Regression and Gradient Descent</p><p style='margin: 0'>Regression: Ordinary Least Square Regression and Gradient Descent</p></td></tr><tr><td style='padding: 15px;'><p>12. Regularised Method for Regression</p><p style='margin: 0'>Regression: Regularised Method for Regression</p></td></tr><tr><td style='padding: 15px;'><p>13. Polynomial Regression</p><p style='margin: 0'>Regression: Polynomial Regression</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Non-linear relationships</p><p style='margin: 0'>Regression: Dealing with Non-linear relationships</p></td></tr><tr><td style='padding: 15px;'><p>15. Feature Importance Revisited</p><p style='margin: 0'>Regression: Feature Importance Revisited</p></td></tr><tr><td style='padding: 15px;'><p>16. Data Pre-Processing 1</p><p style='margin: 0'>Regression: Data Pre-Processing 1</p></td></tr><tr><td style='padding: 15px;'><p>17. Data Pre-Processing 2</p><p style='margin: 0'>Regression: Data Pre-Processing 2</p></td></tr><tr><td style='padding: 15px;'><p>18. Variance Bias Trade Off - Validation Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Validation Curve</p></td></tr><tr><td style='padding: 15px;'><p>19. Variance Bias Trade Off - Learning Curve</p><p style='margin: 0'>Regression: Variance Bias Trade Off - Learning Curve</p></td></tr><tr><td style='padding: 15px;'><p>20. Cross Validation</p><p style='margin: 0'>Regression: Cross Validation</p></td></tr></tbody></table><br><h4>4. Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Classification: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression 1</p><p style='margin: 0'>Classification: Logistic Regression 1</p></td></tr><tr><td style='padding: 15px;'><p>3. Logistic Regression 2</p><p style='margin: 0'>Classification: Logistic Regression 2</p></td></tr><tr><td style='padding: 15px;'><p>4. MNIST Project 1 - Introduction</p><p style='margin: 0'>Classification: MNIST Project 1 - Introduction</p></td></tr><tr><td style='padding: 15px;'><p>5. MNIST Project 2 - SGDClassifiers</p><p style='margin: 0'>Classification: MNIST Project 2 - SGDClassifier</p></td></tr><tr><td style='padding: 15px;'><p>6. MNIST Project 3 - Performance Measures</p><p style='margin: 0'>Classification: MNIST Project 3 - Performance Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p><p style='margin: 0'>Classification: MNIST Project 4 - Confusion Matrix, Precision, Recall and F1 Score</p></td></tr><tr><td style='padding: 15px;'><p>8. MNIST Project 5 - Precision and Recall Tradeoff</p><p style='margin: 0'>Classification: MNIST Project 5 - Precision and Recall Tradeoff</p></td></tr><tr><td style='padding: 15px;'><p>9. MNIST Project 6 - The ROC Curve</p><p style='margin: 0'>Classification: MNIST Project 6 - The ROC Curve</p></td></tr></tbody></table><br><h4>5. Support Vector Machine (SVM)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Support Vector Machine (SVM): Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Support Vector Machine (SVM) Concepts</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Machine (SVM) Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. Linear SVM Classification</p><p style='margin: 0'>Support Vector Machine (SVM): Linear SVM Classification</p></td></tr><tr><td style='padding: 15px;'><p>4. Polynomial Kernel</p><p style='margin: 0'>Support Vector Machine (SVM): Polynomial Kernel</p></td></tr><tr><td style='padding: 15px;'><p>5. Gaussian Radial Basis Function</p><p style='margin: 0'>Support Vector Machine (SVM): Gaussian Radial Basis Function</p></td></tr><tr><td style='padding: 15px;'><p>6. Support Vector Regression</p><p style='margin: 0'>Support Vector Machine (SVM): Support Vector Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Advantages and Disadvantages of SVM</p><p style='margin: 0'>Support Vector Machine (SVM): Advantages and Disadvantages of SVM</p></td></tr></tbody></table><br><h4>6. Tree</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Tree: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Decision Tree</p><p style='margin: 0'>Tree: What is Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>3. Training a Decision Tree</p><p style='margin: 0'>Tree: Training a Decision Tree</p></td></tr><tr><td style='padding: 15px;'><p>4. Visualising a Decision Trees</p><p style='margin: 0'>Tree: Visualising a Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>5. Decision Tree Learning Algorithm</p><p style='margin: 0'>Tree: Decision Tree Learning Algorithm</p></td></tr><tr><td style='padding: 15px;'><p>6. Decision Tree Regression</p><p style='margin: 0'>Tree: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>7. Overfitting and Grid Search</p><p style='margin: 0'>Tree: Overfitting and Grid Search</p></td></tr><tr><td style='padding: 15px;'><p>8. Where to From Here</p><p style='margin: 0'>Tree: Where to From Here</p></td></tr><tr><td style='padding: 15px;'><p>9. Project HR - Loading and preprocesing data</p><p style='margin: 0'>Tree: Project HR - Loading and preprocesing data</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Modelling</p><p style='margin: 0'>Tree: Project HR - Modelling</p></td></tr></tbody></table><br><h4>7. Ensemble Machine Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Ensemble Learning Methods Introduction</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble Learning Methods Introduction</p></td></tr><tr><td style='padding: 15px;'><p>3. Bagging Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Bagging Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Bagging Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Random Forests</p><p style='margin: 0'>Ensemble Machine Learning: Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>6. Extra-Trees</p><p style='margin: 0'>Ensemble Machine Learning: Extra-Trees</p></td></tr><tr><td style='padding: 15px;'><p>7. AdaBoost</p><p style='margin: 0'>Ensemble Machine Learning: Decision Tree Regression</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Boosting Machine</p><p style='margin: 0'>Ensemble Machine Learning: Gradient Boosting Machine</p></td></tr><tr><td style='padding: 15px;'><p>9. XGBoost</p><p style='margin: 0'>Ensemble Machine Learning: XGBoost</p></td></tr><tr><td style='padding: 15px;'><p>10. Project HR - Human Resources Analytics</p><p style='margin: 0'>Ensemble Machine Learning: Project HR - Human Resources Analytics</p></td></tr><tr><td style='padding: 15px;'><p>11. Ensemble of ensembles Part 1</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 1</p></td></tr><tr><td style='padding: 15px;'><p>12. Ensemble of ensembles Part 2</p><p style='margin: 0'>Ensemble Machine Learning: Ensemble of ensembles Part 2</p></td></tr></tbody></table><br><h4>8. k-Nearest Neighbours (kNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. kNN Introduction</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. kNN Concepts</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. kNN and Iris Dataset Demo</p><p style='margin: 0'>K-Nearest Neighbours (kNN): kNN and Iris Dataset Demo</p></td></tr><tr><td style='padding: 15px;'><p>4. Distance Metric</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Distance Metric</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Cancer Detection Part 1</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Cancer Detection Part 2</p><p style='margin: 0'>K-Nearest Neighbours (kNN): Project Cancer Detection Part 2</p></td></tr></tbody></table><br><h4>9. Dimensionality Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Dimensionality Reduction: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Dimensionality Reduction Concept</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Concept</p></td></tr><tr><td style='padding: 15px;'><p>3. PCA Introduction</p><p style='margin: 0'>Dimensionality Reduction: PCA Introduction</p></td></tr><tr><td style='padding: 15px;'><p>4. Dimensionality Reduction Demo</p><p style='margin: 0'>Dimensionality Reduction: Dimensionality Reduction Demo</p></td></tr><tr><td style='padding: 15px;'><p>5. Project Wine 1: Dimensionality Reduction with PCA</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 1: Dimensionality Reduction with PCA</p></td></tr><tr><td style='padding: 15px;'><p>6. Project Wine 2: Choosing the Number of Components</p><p style='margin: 0'>Dimensionality Reduction: Project Wine 2: Choosing the Number of Components</p></td></tr><tr><td style='padding: 15px;'><p>7. Kernel PCA</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA</p></td></tr><tr><td style='padding: 15px;'><p>8. Kernel PCA Demo</p><p style='margin: 0'>Dimensionality Reduction: Kernel PCA Demo</p></td></tr><tr><td style='padding: 15px;'><p>9. LDA & Comparison between LDA and PCA</p><p style='margin: 0'>Dimensionality Reduction: LDA &amp; Comparison between LDA and PCA</p></td></tr></tbody></table><br><h4>10. Unsupervised Learning: Clustering</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>Unsupervised Learning: Clustering: Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Clustering Concepts</p><p style='margin: 0'>Unsupervised Learning: Clustering: Clustering Concepts</p></td></tr><tr><td style='padding: 15px;'><p>3. MLextend</p><p style='margin: 0'>Unsupervised Learning: Clustering: MLextend</p></td></tr><tr><td style='padding: 15px;'><p>4. Ward's Agglomerative Hierarchical Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: Ward's Agglomerative Hierarchical Clustering</p></td></tr><tr><td style='padding: 15px;'><p>5. Truncating Dendrogram</p><p style='margin: 0'>Unsupervised Learning: Clustering: Truncating Dendrogram</p></td></tr><tr><td style='padding: 15px;'><p>6. k-Means Clustering</p><p style='margin: 0'>Unsupervised Learning: Clustering: k-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>7. Elbow Method</p><p style='margin: 0'>Unsupervised Learning: Clustering: Elbow Method</p></td></tr><tr><td style='padding: 15px;'><p>8. Silhouette Analysis</p><p style='margin: 0'>Unsupervised Learning: Clustering: Silhouette Analysis</p></td></tr><tr><td style='padding: 15px;'><p>9. Mean Shift</p><p style='margin: 0'>Unsupervised Learning: Clustering: Mean Shift</p></td></tr></tbody></table>

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning &amp; More!

The Complete Machine Learning Course with Python

<p>In many industries, machine learning (ML) for predictive maps using Python and Leaflet is practical. So, it is highly valued in the UK and worldwide. These lessons are available in one convenient location with our QLS-endorsed course, 'Machine Learning for Predictive Maps in Python and Leaflet.'</p><p>The curriculum for the course 'Machine Learning for Predictive Maps in Python and Leaflet' is organised methodically, beginning with an introduction that acquaints students with the goals of the course. The steps for creating the entire application are provided in several parts, which start from writing the Django Server-Side code to the project source code. After that, the course moves via leaflet programming, allowing students to make dynamic, eye-catching maps. Ultimately, students receive a concrete resource for practice and future reference in the project source code.</p><p>This premium QLS-endorsed Machine Learning for Predictive Maps in Python and Leaflet course has intensive study modules, dedicated tutor assistance, customer support, flexible learning opportunities, and a globally recognised certification. Thus, completing our Machine Learning for Predictive Maps in Python and Leaflet course will give you limitless opportunities anywhere and everywhere since the value of QLS-endorsed courses is relatively high!!</p><p>Enrol in this Python and Leaflet course on machine learning for predictive maps now to transform decision-making procedures and influence industry trends through predictive mapping solutions.</p><p><strong>Learning Outcome of the 'Machine Learning for Predictive Maps in Python and Leaflet' Course</strong><strong>:</strong></p><ul><li>Learn to use predictive mapping techniques based on Python to make informed decisions across a range of industries</li><li>Gain a thorough understanding of Leaflet programming to create dynamic and eye-catching maps</li><li>Build a Django server-side framework to lay a solid basis for developing applications and handling data</li><li>Simplify and improve efficiency by automating machine learning pipelines</li><li>Develop your application deployment skills by applying what you have learned to real-world situations</li><li>Access project source code for continued learning and reference, empowering further exploration and refinement of skills</li></ul><p><strong>Why Learn </strong><strong>Machine Learning for Predictive Maps in Python and Leaflet </strong><strong>with us:</strong></p><p>As one of the leading e-learning service providers globally, we strive to provide all our learners with the best e-learning experience possible that can make a real difference in their career progression.</p><p><strong>Our Service for Machine Learning for Predictive Maps in Python and Leaflet</strong><strong>:</strong></p><ul><li>Intensive Study Notes</li><li>Tutor Support</li><li>Customer Support</li><li>Teaching Assistant Assignment Help</li><li>QLS Endorsed Teaching Assistant Certificate</li><li>24/7 Learning Portal Access</li><li>Widely Compatible Teaching Assistant Study Materials</li><li>14 Days Refund Guarantee</li></ul><p><strong>Course Curriculum</strong></p><p><strong>Machine Learning for Predictive Maps in Python and Leaflet</strong></p><ul><li>Section 01: Introduction</li><li>Section 02: Setup and Installations</li><li>Section 03: Writing the Django Server-Side Code</li><li>Section 04: Writing the Application Front-end Code</li><li>Section 05: Machine Learning</li><li>Section 06: Automating the Machine Learning Pipeline</li><li>Section 07: Leaflet Programming</li><li>Section 08: Project Source Code</li></ul><p><strong>Assessment &amp; Certification</strong></p><p>Students must finish and turn in a thorough assignment covering all topics in our curriculum. Our expert tutor will review the assignment to ensure it meets Quality Learning Systems (QLS) standards. Once the assessment is successful and passes a quality check, students will be awarded a certificate for completing the Machine Learning for Predictive Maps in Python and Leaflet Course.</p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme.</strong></p><p>After the course, participants will receive an endorsed certificate as proof of successful completion. The learner may request a certificate if they complete all course assessments.</p><p><strong>Endorsement:</strong></p><p>After the course, participants will receive an endorsed certificate as proof of successful completion. The learner may request a certificate if they complete all course assessments.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        150 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>This Machine Learning for Predictive Maps in Python and Leaflet course is beneficial for a wide range of learners. It is especially suitable for the following learners:</p><ul><li><strong>Data scientists and analysts:</strong> Individuals seeking to broaden their expertise in predictive modelling and spatial analysis</li><li><strong>Professionals in Geographic Information Systems (GIS): </strong>Looking to incorporate machine learning into their workflows for mapping and spatial analysis</li><li><strong>Developers and Programmers:</strong> keen on integrating predictive mapping into their projects or applications.</li><li><strong>Urban Planners and Environmental Researchers:</strong> Investigating predictive mapping in the fields of planning, sustainability, and environmental impact</li><li><strong>Logistics and Supply Chain Analysts:</strong> Using predictive mapping to optimise routes and manage supply chains</li><li><strong>Pupils studying geography or computer science: </strong>Interested in finding out how machine learning and cartography are combining</li><li><strong>Decision-Making Roles Professionals:</strong> Looking for Tools and Insights to Support Strategic Decision-Making with Predictive Mapping</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No specific requirements exist for enrolling in the Machine Learning for Predictive Maps in Python and Leafletcourse. Therefore, learners do not need previous qualifications to sign up for this course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>'Machine Learning for Predictive Maps in Python and Leaflet' course is relevant to many trending professions as follows:</p><ul><li>Data Scientist/Spatial Analysis. £40,000-£70,000 per year.</li><li>Python developer (with GIS expertise). £35,000-£60,000 per year.</li><li>Machine learning engineer. £50,000-£80,000 per year.</li><li>Urban Planner/Analyst. £30,000-£50,000 per year.</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Diploma in Machine Learning for Predictive Maps in Python and Leaflet at QLS</h4>
        <p>
            Hard copy certificate -
            £75
        </p>

                        </div>

Kingston Open College

Learning Support Assistant (LSA)

Managed Learning Programme (MLP)

ELS (Effective Learning Service)

Learning To Learn

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support

Machine Learning for Predictive Maps in Python and Leaflet - Level 5 (QLS Endorsed)

Duco Digital Training

IT Certifications

IT Skills

IT (Information Technology)

Computing & IT

IT Literacy

Thinking about learning more about Artificial Intelligence?

The BCS Foundation Certificate in Artificial Intelligence is the advanced version of our Essentials Course Artificial Intelligence and includes more detail and insights about algebraic equations, vector calculus and schematics used in artificial intelligence and machine learning for you to learn how this new technology works.

Artificial Intelligence - BCS Foundation Certificate

<strong>The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R</strong>
        <p>This course is your complete guide to both supervised and unsupervised learning using R. This course covers all the main aspects of practical data science; if you take this course, there is no need to take other courses or buy books on R-based data science. In this age of big data, companies across the Globe use R to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in R, you can give your company a competitive edge and take your career to the next level.

Over the course of research, the author realized that almost all the R data science courses and books out there do take account of the multidimensional nature of the topic. This course will give you a robust grounding in the main aspects of machine learning: clustering and classification. Unlike other R instructors, the author digs deep into R's machine learning features and give you a one-of-a-kind grounding in data science! You will go all the way from carrying out data reading & cleaning to machine learning, to finally implementing powerful machine learning algorithms and evaluating their performance via R.
The following topics will be covered: - 
&bull; A full introduction to the R Framework for data science 
&bull; Data structures and reading in R, including CSV, Excel, and HTML data 
&bull; How to pre-process and clean data by removing NAs/No data, visualization 
&bull; Machine learning, supervised learning, and unsupervised learning in R 
&bull; Model building and selection and much more!

The course will help you implement methods using real data obtained from different sources. Many courses use made-up data that does not empower students to implement R-based data science in real life. After taking this course, you'll easily use data science packages such as Caret to work with real data in R. You'll even understand concepts such as unsupervised learning, dimension reduction, and supervised learning. 

All the code and supporting files for this course are available at - https://github.com/PacktPublishing/Clustering-and-Classification-with-Machine-Learning-in-R</p>
        <h2>What You Will Learn</h2><p>Read-in data into the R environment from different sources<br>Carry out basic data pre-processing and wrangling in R Studio<br>Implement unsupervised/clustering techniques such as K-means clustering<br>Implement dimensional reduction techniques (PCA) and feature selection<br>Implement supervised learning techniques/classification such as Random Forests<br>Evaluate model performance and learn the best practices for evaluating machine learning model accuracy</p>
        <h2>Audience</h2><p>This course is for students interested in getting started with data science applications in the R Studio environment. Students wishing to learn how to implement unsupervised learning on real data. Anyone with prior exposure to R who wants to get started with practical data science.</p>
        <h2>Approach</h2><p>Every video is packed with hands-on instructions and clear explanations. Real data has been used to demonstrate how to implement these techniques in real life, on your data.</p>
        <h2>Key Features</h2><p>Provides in-depth training in everything you need to know to get started with practical R data science * Jargon-free and suitable for people who have a non-mathematical background * In-depth coverage of the latest unsupervised and supervised techniques </p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/clustering-and-classification-with-machine-learning-in-r</p>
        <h2>About the Author</h2><h3>Minerva Singh</h3><p>Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a part-time Data Scientist. As part of her research, she must carry out extensive data analysis, including spatial data analysis. For this purpose, she prefers to use a combination of freeware tools: R, QGIS, and Python. She does most of her spatial data analysis work using R and QGIS. Apart from being free, these are very powerful tools for data visualization, processing, and analysis. She also holds an MPhil degree in Geography and Environment from Oxford University. She has honed her statistical and data analysis skills through several MOOCs, including The Analytics Edge and Statistical. In addition to spatial data analysis, she is also proficient in statistical analysis, machine learning, and data mining.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome to Clustering & Classification with Machine Learning in R</p><p style='margin: 0'>Introduction to the Course: Welcome to Clustering & Classification with Machine Learning in R</p></td></tr><tr><td style='padding: 15px;'><p>2. Installing R and R Studio</p><p style='margin: 0'>Introduction to the Course: Installing R and R Studio</p></td></tr></tbody></table><br><h4>2. Read in Data from Different Sources in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Read in CSV & Excel Data</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in CSV & Excel Data</p></td></tr><tr><td style='padding: 15px;'><p>2. Read in Unzipped Folder</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Unzipped Folder</p></td></tr><tr><td style='padding: 15px;'><p>3. Read in Online CSV</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Online CSV</p></td></tr><tr><td style='padding: 15px;'><p>4. Read in Googlesheets</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Googlesheets</p></td></tr><tr><td style='padding: 15px;'><p>5. Read in Data from Online HTML Tables-Part 1</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Data from Online HTML Tables-Part 1</p></td></tr><tr><td style='padding: 15px;'><p>6. Read in Data from Online HTML Tables-Part 2</p><p style='margin: 0'>Read in Data from Different Sources in R: Read in Data from Online HTML Tables-Part 2</p></td></tr><tr><td style='padding: 15px;'><p>7. Read Data from a Database</p><p style='margin: 0'>Read in Data from Different Sources in R: Read Data from a Database</p></td></tr></tbody></table><br><h4>3. Data Pre-processing and Visualization</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Remove Missing Values</p><p style='margin: 0'>Data Pre-processing and Visualization: Remove Missing Values</p></td></tr><tr><td style='padding: 15px;'><p>2. More Data Cleaning</p><p style='margin: 0'>Data Pre-processing and Visualization: More Data Cleaning</p></td></tr><tr><td style='padding: 15px;'><p>3. Introduction to dplyr for Data Summarizing-Part 1</p><p style='margin: 0'>Data Pre-processing and Visualization: Introduction to dplyr for Data Summarizing-Part 1</p></td></tr><tr><td style='padding: 15px;'><p>4. Introduction to dplyr for Data Summarizing-Part 2</p><p style='margin: 0'>Data Pre-processing and Visualization: Introduction to dplyr for Data Summarizing-Part 2</p></td></tr><tr><td style='padding: 15px;'><p>5. Exploratory Data Analysis (EDA): Basic Visualizations with R</p><p style='margin: 0'>Data Pre-processing and Visualization: Exploratory Data Analysis (EDA): Basic Visualizations with R</p></td></tr><tr><td style='padding: 15px;'><p>6. More Exploratory Data Analysis with xda</p><p style='margin: 0'>Data Pre-processing and Visualization: More Exploratory Data Analysis with xda</p></td></tr><tr><td style='padding: 15px;'><p>7. Data Exploration & Visualization With dplyr & ggplot2</p><p style='margin: 0'>Data Pre-processing and Visualization: Data Exploration & Visualization With dplyr & ggplot2</p></td></tr><tr><td style='padding: 15px;'><p>8. Associations Between Quantitative Variables- Theory</p><p style='margin: 0'>Data Pre-processing and Visualization: Associations Between Quantitative Variables- Theory</p></td></tr><tr><td style='padding: 15px;'><p>9. Testing for Correlation</p><p style='margin: 0'>Data Pre-processing and Visualization: Testing for Correlation</p></td></tr><tr><td style='padding: 15px;'><p>10. Evaluate the Relation Between Nominal Variables</p><p style='margin: 0'>Data Pre-processing and Visualization: Evaluate the Relation Between Nominal Variables</p></td></tr><tr><td style='padding: 15px;'><p>11. Cramer's V for Examining the Strength of Association Between Nominal Variable</p><p style='margin: 0'>Data Pre-processing and Visualization: Cramer's V for Examining the Strength of Association Between Nominal Variable</p></td></tr></tbody></table><br><h4>4. Machine Learning for Data Science</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How is Machine Learning Different from Statistical Data Analysis?</p><p style='margin: 0'>Machine Learning for Data Science: How is Machine Learning Different from Statistical Data Analysis?</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Machine Learning (ML) About? Some Theoretical Pointers</p><p style='margin: 0'>Machine Learning for Data Science: What is Machine Learning (ML) About? Some Theoretical Pointers</p></td></tr></tbody></table><br><h4>5. Unsupervised Learning in R</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. K-Means Clustering</p><p style='margin: 0'>Unsupervised Learning in R: K-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>2. Other Ways of Selecting Cluster Numbers</p><p style='margin: 0'>Unsupervised Learning in R: Other Ways of Selecting Cluster Numbers</p></td></tr><tr><td style='padding: 15px;'><p>3. Fuzzy K-Means Clustering</p><p style='margin: 0'>Unsupervised Learning in R: Fuzzy K-Means Clustering</p></td></tr><tr><td style='padding: 15px;'><p>4. Weighted k-means</p><p style='margin: 0'>Unsupervised Learning in R: Weighted k-means</p></td></tr><tr><td style='padding: 15px;'><p>5. Partitioning Around Meloids (PAM)</p><p style='margin: 0'>Unsupervised Learning in R: Partitioning Around Meloids (PAM)</p></td></tr><tr><td style='padding: 15px;'><p>6. Hierarchical Clustering in R</p><p style='margin: 0'>Unsupervised Learning in R: Hierarchical Clustering in R</p></td></tr><tr><td style='padding: 15px;'><p>7. Expectation-Maximization (EM) in R</p><p style='margin: 0'>Unsupervised Learning in R: Expectation-Maximization (EM) in R</p></td></tr><tr><td style='padding: 15px;'><p>8. DBSCAN Clustering in R</p><p style='margin: 0'>Unsupervised Learning in R: DBSCAN Clustering in R</p></td></tr><tr><td style='padding: 15px;'><p>9. Cluster a Mixed Dataset</p><p style='margin: 0'>Unsupervised Learning in R: Cluster a Mixed Dataset</p></td></tr><tr><td style='padding: 15px;'><p>10. Should We Even Do Clustering?</p><p style='margin: 0'>Unsupervised Learning in R: Should We Even Do Clustering?</p></td></tr><tr><td style='padding: 15px;'><p>11. Assess Clustering Performance</p><p style='margin: 0'>Unsupervised Learning in R: Assess Clustering Performance</p></td></tr><tr><td style='padding: 15px;'><p>12. Which Clustering Algorithm to Choose?</p><p style='margin: 0'>Unsupervised Learning in R: Which Clustering Algorithm to Choose?</p></td></tr></tbody></table><br><h4>6. Feature/Dimension Reduction</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Dimension Reduction-theory</p><p style='margin: 0'>Feature/Dimension Reduction: Dimension Reduction-theory</p></td></tr><tr><td style='padding: 15px;'><p>2. Principal Component Analysis (PCA)</p><p style='margin: 0'>Feature/Dimension Reduction: Principal Component Analysis (PCA)</p></td></tr><tr><td style='padding: 15px;'><p>3. More on PCA</p><p style='margin: 0'>Feature/Dimension Reduction: More on PCA</p></td></tr><tr><td style='padding: 15px;'><p>4. Multidimensional Scaling</p><p style='margin: 0'>Feature/Dimension Reduction: Multidimensional Scaling</p></td></tr><tr><td style='padding: 15px;'><p>5. Singular Value Decomposition (SVD)</p><p style='margin: 0'>Feature/Dimension Reduction: Singular Value Decomposition (SVD)</p></td></tr></tbody></table><br><h4>7. Feature Selection to Select the Most Relevant Predictors</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Removing Highly Correlated Predictor Variables</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Removing Highly Correlated Predictor Variables</p></td></tr><tr><td style='padding: 15px;'><p>2. Variable Selection Using LASSO Regression</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Variable Selection Using LASSO Regression</p></td></tr><tr><td style='padding: 15px;'><p>3. Variable Selection with FSelector</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Variable Selection with FSelector</p></td></tr><tr><td style='padding: 15px;'><p>4. Boruta Analysis for Feature Selection</p><p style='margin: 0'>Feature Selection to Select the Most Relevant Predictors: Boruta Analysis for Feature Selection</p></td></tr></tbody></table><br><h4>8. Supervised Learning Theory</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Some Basic Supervised Learning Concepts</p><p style='margin: 0'>Supervised Learning Theory: Some Basic Supervised Learning Concepts</p></td></tr><tr><td style='padding: 15px;'><p>2. Pre-processing for Supervised Learning</p><p style='margin: 0'>Supervised Learning Theory: Pre-processing for Supervised Learning</p></td></tr></tbody></table><br><h4>9. Supervised Learning: Classification</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What are GLMs?</p><p style='margin: 0'>Supervised Learning: Classification: What are GLMs?</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression Models as Binary Classifiers</p><p style='margin: 0'>Supervised Learning: Classification: Logistic Regression Models as Binary Classifiers</p></td></tr><tr><td style='padding: 15px;'><p>3. Binary Classifier with PCA</p><p style='margin: 0'>Supervised Learning: Classification: Binary Classifier with PCA</p></td></tr><tr><td style='padding: 15px;'><p>4. Some Pointers on Evaluating Accuracy</p><p style='margin: 0'>Supervised Learning: Classification: Some Pointers on Evaluating Accuracy</p></td></tr><tr><td style='padding: 15px;'><p>5. Obtain Binary Classification Accuracy Metrics</p><p style='margin: 0'>Supervised Learning: Classification: Obtain Binary Classification Accuracy Metrics</p></td></tr><tr><td style='padding: 15px;'><p>6. More on Binary Accuracy Measures</p><p style='margin: 0'>Supervised Learning: Classification: More on Binary Accuracy Measures</p></td></tr><tr><td style='padding: 15px;'><p>7. Linear Discriminant Analysis</p><p style='margin: 0'>Supervised Learning: Classification: Linear Discriminant Analysis</p></td></tr><tr><td style='padding: 15px;'><p>8. Our Multi-class Classification Problem</p><p style='margin: 0'>Supervised Learning: Classification: Our Multi-class Classification Problem</p></td></tr><tr><td style='padding: 15px;'><p>9. Classification Trees</p><p style='margin: 0'>Supervised Learning: Classification: Classification Trees</p></td></tr><tr><td style='padding: 15px;'><p>10. More on Classification Tree Visualization</p><p style='margin: 0'>Supervised Learning: Classification: More on Classification Tree Visualization</p></td></tr><tr><td style='padding: 15px;'><p>11. Classification with Party Package</p><p style='margin: 0'>Supervised Learning: Classification: Classification with Party Package</p></td></tr><tr><td style='padding: 15px;'><p>12. Decision Trees</p><p style='margin: 0'>Supervised Learning: Classification: Decision Trees</p></td></tr><tr><td style='padding: 15px;'><p>13. Random Forest (RF) Classification</p><p style='margin: 0'>Supervised Learning: Classification: Random Forest (RF) Classification</p></td></tr><tr><td style='padding: 15px;'><p>14. Examine Individual Variable Importance for Random Forests</p><p style='margin: 0'>Supervised Learning: Classification: Examine Individual Variable Importance for Random Forests</p></td></tr><tr><td style='padding: 15px;'><p>15. GBM Classification</p><p style='margin: 0'>Supervised Learning: Classification: GBM Classification</p></td></tr><tr><td style='padding: 15px;'><p>16. Support Vector Machines (SVM) for Classification</p><p style='margin: 0'>Supervised Learning: Classification: Support Vector Machines (SVM) for Classification</p></td></tr><tr><td style='padding: 15px;'><p>17. More SVM for Classification</p><p style='margin: 0'>Supervised Learning: Classification: More SVM for Classification</p></td></tr><tr><td style='padding: 15px;'><p>18. Variable Importance in SVM Modelling with rminer</p><p style='margin: 0'>Supervised Learning: Classification: Variable Importance in SVM Modelling with rminer</p></td></tr></tbody></table><br><h4>10. Additional Lectures</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Fuzzy C-Means Clustering</p><p style='margin: 0'>Additional Lectures: Fuzzy C-Means Clustering</p></td></tr></tbody></table>

HTML

The underlying patterns in your data hold vital insights; unearth them with cutting-edge clustering and classification techniques in R

Clustering and Classification with Machine Learning in R

<div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
			<h2><b>Course Overview</b></h2>		</div>
				</div>
				<div>
				<div>
					<div><p><span>Machine learning as a programming technique has shaped the future of technology. In this course, you will learn how to build intelligent handwriting recognition apps from scratch using Python and Core ML.</span></p><p><span>The </span><b>Machine Learning for Apps Level 4 </b><span>course will teach you how to take advantage of machine learning to code like a pro and build incredible apps that can make predictions. Designed by industry experts, it covers best practices for managing projects, core concepts for creating your own ML model, building a convolutional neural network, and much more.</span></p><p><span>On successful completion, you will be able to build an amazing handwriting recognition app and convolutional neural network from scratch, and have an in-depth understanding of the core ML basics. This course is ideal for those with a basic understanding of iOS development.</span></p></div>
				</div>
				</div>
				<div>
				<div>
					<div><p>This best selling <strong>Machine Learning for Apps Level 4 </strong>has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth <strong>Machine Learning for Apps Level 4 </strong>is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The <strong>Machine Learning for Apps Level 4 </strong>is CPD-accredited, so you can be confident you're completing a quality training course will boost your CV and enhance your career potential. The <strong>Machine Learning for Apps Level 4 </strong>is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the<strong> Machine Learning for Apps Level 4</strong>, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the <strong>Machine Learning for Apps Level 4 </strong>will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the <strong>Machine Learning for Apps Level 4 </strong>to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
				</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div><div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
					</div>
				</div>
				<section>
						<div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
					<div>
											
															
											</div>
				</div>
				</div>
						</div>
					</div>
		</div>
				<div>
			<div>
							<div>
						<div>
				<div>
					<div>
											
															
											</div>
				</div>
				</div>
						</div>
					</div>
		</div>
				<div>
			<div>
							<div>
						<div>
				<div>
					<div>
											
															
											</div>
				</div>
				</div>
						</div>
					</div>
		</div>
				<div>
			<div>
							<div>
						<div>
				<div>
					<div>
											
															
											</div>
				</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>
		</section>
						</div>
					</div>
		</div>
								</div>
					</div>

Course Cloud

Machine Learning for Apps Level 4

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p>This course is intended for:<br />Developers<br />Solutions Architects<br />Data Engineers<br />Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon<br />SageMaker</p>Overview<p>In this course, you will learn to:<br />Select and justify the appropriate ML approach for a given business problem<br />Use the ML pipeline to solve a specific business problem<br />Train, evaluate, deploy, and tune an ML model using Amazon SageMaker<br />Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS<br />Apply machine learning to a real-life business problem after the course is complete</p><p>This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.</p></div><div id="course-pills-2"><h4>Module 0: Introduction</h4><ul><li>Pre-assessment</li></ul><h4>Module 1: Introduction to Machine Learning and the ML Pipeline </h4><ul><li>Overview of machine learning, including use cases, types of machine learning, and key concepts<br />Overview of the ML pipeline<br />Introduction to course projects and approach</li></ul><h4>Module 2: Introduction to Amazon SageMaker</h4><ul><li>Introduction to Amazon SageMaker</li><li>Demo: Amazon SageMaker and Jupyter notebooks</li><li>Hands-on: Amazon SageMaker and Jupyter notebooks</li></ul><h4>Module 3: Problem Formulation</h4><ul><li>Overview of problem formulation and deciding if ML is the right solution</li><li>Converting a business problem into an ML problem</li><li>Demo: Amazon SageMaker Ground Truth</li><li>Hands-on: Amazon SageMaker Ground Truth</li><li>Practice problem formulation</li><li>Formulate problems for projects</li></ul><h4>Module 4: Preprocessing</h4><ul><li>Overview of data collection and integration, and techniques for data preprocessing and visualization<br />Practice preprocessing<br />Preprocess project data<br />Class discussion about projects</li></ul><h4>Module 5: Model Training</h4><ul><li>Choosing the right algorithm<br />Formatting and splitting your data for training<br />Loss functions and gradient descent for improving your model<br />Demo: Create a training job in Amazon SageMaker</li></ul><h4>Module 6: Model Evaluation</h4><ul><li>How to evaluate classification models</li><li>How to evaluate regression models</li><li>Practice model training and evaluation</li><li>Train and evaluate project models</li><li>Initial project presentations</li></ul><h4>Module 7: Feature Engineering and Model Tuning</h4><ul><li>Feature extraction, selection, creation, and transformation</li><li>Hyperparameter tuning</li><li>Demo: SageMaker hyperparameter optimization</li><li>Practice feature engineering and model tuning</li><li>Apply feature engineering and model tuning to projects</li><li>Final project presentations</li></ul><h4>Module 8: Deployment</h4><ul><li>How to deploy, inference, and monitor your model on Amazon SageMaker<br />Deploying ML at the edge<br />Demo: Creating an Amazon SageMaker endpoint<br />Post-assessment<br />Course wrap-up</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans The Machine Learning Pipeline on AWS training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the The Machine Learning Pipeline on AWS course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

16 Machine Learning (ML) courses in Leicester

Fundamentals of E&P Data Management

By EnergyEdge - Training for a Sustainable Energy Future

Certified Data Science Practitioner

By Mpi Learning - Professional Learning And Development Provider

Introduction to Data Science

By futureCoders SE

AI For Leaders

By Mpi Learning - Professional Learning And Development Provider

Data Ethics for Business Professionals

By Mpi Learning - Professional Learning And Development Provider

Introduction to Data Science - free for Medway residents

By futureCoders SE

Educators matching "Machine Learning (ML)"

mpi learning - professional learning and development provider

Loughborough

Search By Location