Name: Python for Beginner, Intermediate & Advanced - QLS Endorsed Level 7 Course
Price: 49 GBP
Availability: InStock

<strong>Let's use ChatGPT to build a pairs trading bot in Python and understand pairs, algorithmic, algo-trading, and stock trading strategies. Compute z-scores, log, cumulative, and portfolio returns. Apply data science strategies to financial analysis and trading strategies for stocks, forex, cryptocurrencies, Bitcoin, Ethereum, and altcoins.</strong>
        <p>Financial analysis with ChatGPT using pairs is a cutting-edge approach that combines the power of artificial intelligence (AI) with the expertise of financial analysts to provide insights into financial data. ChatGPT and human analysts synergistically foster a collaborative environment that facilitates and generates valuable investment decisions, risk assessments, and financial planning recommendations. Financial analysis with ChatGPT using pairs offers a unique approach to insights from complex financial data.

Through this course, we will use ChatGPT to build a trading bot (using pairs trading) and learn about the capabilities of ChatGPT. The course begins with an introduction to ChatGPT, the project scope, and the course tools required for this course. You will then learn to use the course efficiently and where to get the codes. We will then advance to Pairs trading with ChatGPT. You will learn about pairs trading intuition, the initial prompt, correcting the trading signal, and z-score computation. We will explore returns, log returns, and cumulative returns and test the strategy. You will learn about the long-only strategy and return computation.

Upon completing this course, you will learn the best ways to use ChatGPT to be more efficient and productive with financial decision-making, investments, and trading using pairs trading.</p>
        <h2>What You Will Learn</h2><p>Learn to use ChatGPT to build a pairs trading bot in Python<br>Learn the common mistakes when using ChatGPT for coding<br>Develop Pairs, algorithmic, algo-trading, and stock trading strategies<br>Compute z-scores, log, cumulative returns, and portfolio returns<br>Understand how to apply data science strategies to financial analysis<br>Learn trading strategies for stocks, forex, cryptos, Bitcoin, Ethereum</p>
        <h2>Audience</h2><p>This course is designed for individuals who want to learn to use ChatGPT to build a pairs trading bot or students and professionals in data science and machine learning interested in financial analysis. The prerequisites for the course include a decent understanding of Python and data science libraries (NumPy, Matplotlib, and Pandas), basic knowledge of finance (stock prices, logs, and cumulative returns), and foundational knowledge in Python, finance, and statistics. Please note that the section on Python coding for beginners is not a comprehensive Python coding tutorial per se.</p>
        <h2>Approach</h2><p>This course by Lazy Programmers is designed to deliver content using a mixed approach with content presentations over ChatGPT AI, live demonstrations of setup and strategies, explanations of content over PowerPoint presentations, and coding live on-screen in a classroom setup. The instructions and concepts are succinctly explained over a well-structured video course.</p>
        <h2>Key Features</h2><p>Build a pairs trading bot with ChatGPT and acquire pairs, algorithmic, and stock trading strategies * Perform financial analysis using z-scores, log, cumulative, and portfolio returns computed from the data * Develop trading strategies using pairs for stocks, forex, cryptos, Bitcoin, Ethereum, and altcoins</p>
        
        <h2>About the Author</h2><h3>Lazy Programmer</h3><p>The Lazy Programmer is an AI/ML engineer focusing on deep learning with experience in data science, big data engineering, and full-stack development. With a background in computer engineering and specialization in ML, he holds two master's degrees in computer engineering and statistics with finance applications. His online advertising and digital media expertise include data science and big data.

He has created DL models for prediction and has experience in recommender systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He is a web programmer with experience in Python, Ruby/Rails, PHP, and Angular.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><br><h4>2. Getting Setup</h4><p></p><br><h4>3. Pairs Trading with ChatGPT</h4><p></p><br><h4>4. Sanity Check</h4><p></p><br><h4>5. Course Summary</h4><p></p><br><h4>6. Setting Up Your Environment</h4><p></p><br><h4>7. Extra Help With Python Coding for Beginners</h4><p></p><br><h4>8. Effective Learning Strategies for Machine Learning</h4><p></p><br><h4>9. Appendix / FAQ Finale</h4><p></p>

Packt

Bitcoin

Programming

Let's use ChatGPT to build a pairs trading bot in Python and understand pairs, algorithmic, algo-trading, and stock trading strategies. Compute z-scores, log, cumulative, and portfolio returns. Apply data science strategies to financial analysis and trading strategies for stocks, forex, cryptocurrencies, Bitcoin, Ethereum, and altcoins.

Financial Analysis - Build a ChatGPT Pairs Trading Bot

General Admission

<p>Dive into the transformative world of Artificial Intelligence through the course titled 'Foundations of Artificial Intelligence: Building Intelligent Systems.' This comprehensive curriculum sweeps across an array of subjects, from the rudimentary introduction to AI to the intricate nuances of building AI applications. Embrace a holistic understanding of core modules like Machine Learning, Natural Language Processing, and Robotics. The content, framed meticulously, beckons those inquisitive minds eager to craft, innovate, and change the world with AI's limitless possibilities.</p>
<p>Deepen your conceptual clarity with two-part modules that delve into Knowledge Representation and Machine Learning, ensuring that learners grasp intricate details without feeling overwhelmed. With sections dedicated to Computer Vision and Deep Learning, individuals will find themselves proficiently navigating the vibrant ecosystems these technologies encompass. Finally, a spotlight on AI applications ensures that learners not only acquire theoretical wisdom but also grasp how AI integrates into real-world scenarios.</p>
<p>By the culmination of this course, participants will stand at the forefront of AI innovations, armed with the acumen to shape a future where intelligent systems intertwine seamlessly with our daily lives. This foundation lays the groundwork for boundless exploration in the Artificial Intelligence realm</p>
<p><strong>Learning Outcomes</strong></p>
<p>Upon completion of this course, participants will be able to:</p>
<ul>
<li>Gain comprehensive insights into the fundamental principles of Artificial Intelligence.</li>
<li>Understand the critical mathematical concepts underpinning AI technologies.</li>
<li>Develop proficiency in various AI knowledge representation methods.</li>
<li>Acquire a solid foundation in Machine Learning, Deep Learning, and Natural Language Processing techniques.</li>
<li>Familiarise with the applications and integrations of AI in Robotics and Computer Vision.</li>
</ul>
<h3><b>Why buy this Foundations of Artificial Intelligence: Building Intelligent Systems?</b></h3>
<ol>
<li><span>Unlimited access to the course for forever</span></li>
<li><b>Digital Certificate, Transcript, student ID</b><span> all included in the price</span></li>
<li><span>Absolutely no hidden fees</span></li>
<li><span>Directly receive </span><b>CPD </b><span>accredited qualifications after course completion</span></li>
<li><span>Receive one to one assistance on every weekday from professionals</span></li>
<li><span>Immediately receive the PDF certificate after passing</span></li>
<li><span>Receive the original copies of your certificate and transcript on the next working day</span></li>
<li><span>Easily learn the skills and knowledge from the comfort of your home</span></li>
</ol>
<h3><b>Certification</b></h3>
<p><span>After studying the course materials of the </span><b>Foundations of Artificial Intelligence: Building Intelligent Systems</b><span> there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60.</span></p>
<h3><b>Who is this Foundations of Artificial Intelligence: Building Intelligent Systems course for?</b></h3>
<p>This Foundations of Artificial Intelligence: Building Intelligent Systems does not require you to have any prior qualifications or experience. You can just enrol and start learning.</p>
<ul>
<li>Aspiring AI enthusiasts keen on building a robust foundation in the subject.</li>
<li>Technologists aiming to pivot into AI-centric roles.</li>
<li>Researchers eager to enhance their knowledge spectrum in intelligent systems.</li>
<li>University students studying computer science or related disciplines, looking to supplement their academic pursuits.</li>
<li>Entrepreneurs eyeing opportunities in AI-driven ventures.</li>
</ul>
<h3><b>Prerequisites</b></h3>
<p><span>This </span><b>Foundations of Artificial Intelligence: Building Intelligent Systems</b><span> does not require you to have any prior qualifications or experience. You can just enrol and start learning.This </span><b>Foundations of Artificial Intelligence: Building Intelligent Systems</b><span> was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.</span></p>
<h3><b>Career path</b></h3>
<ul>
<li><strong>AI Research Scientist</strong> - Average Salary Range: £60,000 - £85,000 per annum</li>
<li><strong>Machine Learning Engineer</strong> - Average Salary Range: £55,000 - £80,000 per annum</li>
<li><strong>NLP Specialist</strong> - Average Salary Range: £50,000 - £75,000 per annum</li>
<li><strong>Computer Vision Engineer</strong> - Average Salary Range: £52,000 - £77,000 per annum</li>
<li><strong>Robotics Engineer</strong> - Average Salary Range: £48,000 - £73,000 per annum</li>
<li><strong>AI Application Developer</strong> - Average Salary Range: £54,000 - £79,000 per annum</li>
</ul><div>
				<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4">Module 01: Introduction to Artificial Intelligence</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Introduction to Artificial Intelligence</td>
					<td> </td>
					<td><span><i></i> 00:21:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 02: Mathematics for AI</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Mathematics for AI</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 03: Knowledge Representation in AI - Part 1</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Knowledge Representation in AI - Part 1</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 04: Knowledge Representation in AI - Part 2</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Knowledge Representation in AI - Part 2</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 05: Machine Learning - Part 1</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning - Part 1</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 06: Machine Learning - Part 2</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Machine Learning - Part 2</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 07: Deep Learning</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Deep Learning</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 08: Natural Language Processing</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Natural Language Processing</td>
					<td> </td>
					<td><span><i></i> 00:22:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 09: Computer Vision</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Computer Vision</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 10: Robotics</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Robotics</td>
					<td> </td>
					<td><span><i></i> 00:18:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Module 11: Building AI Applications</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Building AI Applications</td>
					<td> </td>
					<td><span><i></i> 00:24:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

Studyhub UK

AI (Artificial Intelligence)

Foundations of Artificial Intelligence: Building Intelligent Systems

JavaScript

Web Development

Explore ChatGPT, a cutting-edge world of AI content creation, with our comprehensive course. This meticulously curated program unravels the technology behind ChatGPT to practical applications in machine learning, social media, data analysis, and image generation. This course unveils new facets of AI to navigate this evolving landscape with prowess.

ChatGPT and Prompt Engineering With Advanced Data Analysis

<p>Tired of browsing and searching for a <strong>Data Analysis and Data Science </strong> course you are looking for? Can't find the complete package that fulfils all your needs? Then don't worry as you have just found the solution. Take a minute and look through this extensive bundle that has everything you need to succeed.</p> <p>After surveying thousands of learners just like you and considering their valuable feedback, this all-in<strong>-</strong>one Data Analysis and Data Science bundle has been designed by industry experts. We prioritised what learners were looking for in a complete package and developed this in-demand Data Analysis and Data Science course that will enhance your skills and prepare you for the competitive job market.</p> <p>Also, our experts are available for answering your queries on Data Analysis and Data Science and help you along your learning journey. Advanced audio-visual learning modules of these Data Analysis and Data Science courses are broken down into little chunks so that you can learn at your own pace without being overwhelmed by too much material at once.</p> <p>Furthermore, to help you showcase your expertise in Data Analysis and Data Science, we have prepared a special gift of 1 hardcopy certificate and 1 PDF certificate for the title course completely free of cost. These certificates will enhance your credibility and encourage possible employers to pick you over the rest.</p> <p><strong>This Data Analysis and Data Science Bundle Consists of the following Premium courses: </strong></p> <ul> <li><strong> </strong><strong>Course 01: </strong>Introduction to Data Analysis</li><li><strong> </strong><strong>Course 02: </strong>Python for Data Analysis</li><li><strong> </strong><strong>Course 03: </strong>Statistical Analysis</li><li><strong> </strong><strong>Course 04: </strong>SQL NoSQL Big Data and Hadoop</li><li><strong> </strong><strong>Course 05: </strong>Complete Microsoft Power BI 2021</li><li><strong> </strong><strong>Course 06: </strong>Data Analysis in Excel Level 3 Course</li><li><strong> </strong><strong>Course 07: </strong>Data Analytics with Tableau</li><li><strong> </strong><strong>Course 08: </strong>Basic Google Data Studio</li><li><strong> </strong><strong>Course 09: </strong>Business Analytics</li><li><strong> </strong><strong>Course 10: </strong>Complete Introduction to Business Data Analysis Level 3</li><li><strong> </strong><strong>Course 11: </strong>Business Intelligence and Data Mining Masterclass</li><li><strong> </strong><strong>Course 12: </strong>Research Methods in Business</li><li><strong> </strong><strong>Course 13: </strong>Computer Science: Graph Theory Algorithms</li><li><strong> </strong><strong>Course 14: </strong>Data Protection and Data Security Level 2</li></ul> <p>Enrol now in Data Analysis and Data Science to advance your career, and use the premium study materials from Apex Learning.</p> <p><strong>How will I get my Certificate?</strong></p> <p>After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement.</p> <ul> <li><strong>PDF Certificate: Free (For The Title Course)</strong></li><li><strong>Hard Copy Certificate: Free (For The Title Course)</strong></li></ul><p>The bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your Data Analysis and Data Science expertise and essential knowledge, which will assist you in reaching your goal.</p> <p><strong>Curriculum of Bundle</strong></p> <p><strong> Course 01: Introduction to Data Analysis</strong></p> <ul> <li>Module 01: Introduction</li><li>Module 02: Agenda and Principles of Process Management</li><li>Module 03: The Voice of the Process</li><li>Module 04: Working as One Team for Improvement</li><li>Module 05: Exercise: The Voice of the Customer</li><li>Module 06: Tools for Data Analysis</li><li>Module 07: The Pareto Chart</li><li>Module 08: The Histogram</li><li>Module 09: The Run Chart</li><li>Module 10: Exercise: Presenting Performance Data</li><li>Module 11: Understanding Variation</li><li>Module 12: The Control Chart</li><li>Module 13: Control Chart Example</li><li>Module 14: Control Chart Special Cases</li><li>Module 15: Interpreting the Control Chart</li><li>Module 16: Control Chart Exercise</li><li>Module 17: Strategies to Deal with Variation</li><li>Module 18: Using Data to Drive Improvement</li><li>Module 19: A Structure for Performance Measurement</li><li>Module 20: Data Analysis Exercise</li><li>Module 21: Course Project</li><li>Module 22: Test your Understanding</li></ul> <p><strong> Course 02: Python for Data Analysis</strong></p> <ul> <li>Welcome, Course Introduction &amp; overview, and Environment set-up</li><li>Python Essentials</li><li>Python for Data Analysis using NumPy</li><li>Python for Data Analysis using Pandas</li><li>Python for Data Visualization using matplotlib</li><li>Python for Data Visualization using Seaborn</li><li>Python for Data Visualization using pandas</li><li>Python for interactive &amp; geographical plotting using Plotly and Cufflinks</li><li>Capstone Project - Python for Data Analysis &amp; Visualization</li><li>Python for Machine Learning (ML) - scikit-learn - Linear Regression Model</li><li>Python for Machine Learning - scikit-learn - Logistic Regression Model</li><li>Python for Machine Learning - scikit-learn - K Nearest Neighbors</li><li>Python for Machine Learning - scikit-learn - Decision Tree and Random Forests</li><li>Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs)</li><li>Python for Machine Learning - scikit-learn - K Means Clustering</li><li>Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA)</li><li>Recommender Systems with Python - (Additional Topic)</li><li>Python for Natural Language Processing (NLP) - NLTK - (Additional Topic)</li></ul> <p><strong> Course 03: Statistical Analysis</strong></p> <ul> <li>Module 01: The Realm of Statistics</li><li>Module 02: Basic Statistical Terms</li><li>Module 03: The Center of the Data</li><li>Module 04: Data Variability</li><li>Module 05: Binomial and Normal Distributions</li><li>Module 06: Introduction to Probability</li><li>Module 07: Estimates and Intervals</li><li>Module 08: Hypothesis Testing</li><li>Module 09: Regression Analysis</li><li>Module 10: Algorithms, Analytics and Predictions</li><li>Module 11: Learning From Experience: The Bayesian Way</li><li>Module 12: Doing Statistics: The Wrong Way</li><li>Module 13: How We Can Do Statistics Better</li></ul> <p><strong> Course 04: SQL NoSQL Big Data and Hadoop</strong></p> <ul> <li>Module 01: Introduction</li><li>Module 02: Relational Database Systems</li><li>Module 03: Database Classification</li><li>Module 04: Key-Value Store</li><li>Module 05: Document-Oriented Databases</li><li>Module 06: Search Engines</li><li>Module 07: Wide Column Store</li><li>Module 08: Time Series Databases</li><li>Module 09: Graph Databases</li><li>Module 10: Hadoop Platform</li><li>Module 11: Big Data SQL Engines</li><li>Module 12: Distributed Commit Log</li><li>Module 13: Summary</li></ul> <p><strong> Course 05: Complete Microsoft Power BI 2021</strong></p> <ul> <li>Module 01: Introduction</li><li>Module 02: Preparing our Project</li><li>Module 03: Data Transformation - The Query Editor</li><li>Module 04: Data Transformation - Advanced</li><li>Module 05: Creating a Data Model</li><li>Module 06: Data Visualization</li><li>Module 07: Power BI &amp; Python</li><li>Module 08: Storytelling with Data</li><li>Module 09: DAX - The Essentials</li><li>Module 10: DAX - The CALCULATE function</li><li>Module 11: Power BI Service - Power BI Cloud</li><li>Module 12: Row-Level Security</li><li>Module 13: More data sources</li><li>Module 14: Next steps to improve &amp; stay up to date</li></ul> <p><strong> Course 06: Data Analysis in Excel Level 3 Course</strong></p> <ul> <li>Modifying a Worksheet</li><li>Working with Lists</li><li>Analyzing Data</li><li>Visualizing Data with Charts</li><li>Using PivotTables and PivotCharts</li><li>Working with Multiple Worksheets and Workbooks</li><li>Using Lookup Functions and Formula Auditing</li><li>Automating Workbook Functionality</li><li>Creating Sparklines and Mapping Data</li><li>Forecasting Data</li></ul> <p><strong> Course 07: Data Analytics with Tableau</strong></p> <ul> <li>Module 01: Introduction to the Course</li><li>Module 02: Project 1: Discount Mart (Sales and Profit Analytics)</li><li>Module 03: Project 2: Green Destinations (HR Analytics)</li><li>Module 04: Project 3: Superstore (Sales Agent Tracker)</li><li>Module 05: Northwind Trade (Shipping Analytics)</li><li>Module 06: Project 5: Tesla (Stock Price Analytics)</li><li>Module 07: Bonus: Introduction to Database Concepts</li><li>Module 08: Tableau Stories</li></ul> <p><strong> Course 08: Basic Google Data Studio</strong></p> <ul> <li>Module 01: Introduction to GDS</li><li>Module 02: Data Visualization</li><li>Module 03: Geo-visualization</li><li>Module 04: A Socio-Economic Case Study</li></ul> <p><strong> Course 09: Business Analytics</strong></p> <ul> <li>Module 01: What is business analysis?</li><li>Module 02: Strategy analysis</li><li>Module 03: Collaboration</li><li>Module 04: Requirements analysis and Design definition</li><li>Module 05: Requirements lifecycle management</li><li>Module 06: Solution quality</li><li>Module 07: Stakeholder management</li><li>Module 08: BA Governance</li><li>Module 09: Legal notes and Copyright information</li></ul> <p><strong> Course 10: Complete Introduction to Business Data Analysis Level 3</strong></p> <ul> <li>Module 1: Statistics Fundamentals</li><li>Module 2: Data Analysis</li><li>Module 3: Probability</li><li>Module 4: Random Variables and Discrete Distributions</li><li>Module 5: Continuous Distributions</li><li>Module 6: Sampling Distributions</li><li>Module 7: Confidence Interval</li><li>Module 8: Hypothesis Testing with One Sample</li><li>Module 9: Hypothesis Testing with Two Samples</li><li>Module 10: The Chi-Square Distribution</li><li>Module 11: F Distribution and One-Way ANOVA</li><li>Module 12: Correlation analysis</li><li>Module 13: Simple Linear Regression Analysis</li></ul> <p><strong> Course 11: Business Intelligence and Data Mining Masterclass</strong></p> <ul> <li>Module 01: What is Business Intelligence?</li><li>Module 02: Starting Case in understanding BI needs in diff phase of business</li><li>Module 03: Decision Making Process and Need of IT systems</li><li>Module 04: Problem Structure and Decision Support System</li><li>Module 05: Introduction to BI Applications</li><li>Module 06: Dashboard presentation systems</li><li>Module 07: Different Types of Charts used in 131 Dashboards</li><li>Module 08: Good Dashboard and BSC</li><li>Module 09: Examples of Bad Dashboards 1</li><li>Module 10: Examples of Bad Dashboards 2</li></ul> <p>And much more...</p> <p><strong> Course 12: Research Methods in Business</strong></p> <ul> <li>Section 01: Applied Project &amp; Research Methods in Business</li><li>Section 02: Writing a Purpose / Quantitative and Qualitative Research Approaches</li><li>Section 03: Mixed Method Research Approaches, Ethical Considerations &amp; Writing</li><li>Effectively Written Methodology Part 3 !@@</li><li>Section 04: Writing Data Collection Tools, Qualitative &amp; Quantitative Data Analysis</li><li>Section 05: Comparing Findings to Literature and Writing the Final Paper</li></ul> <p><strong> Course 13: Computer Science: Graph Theory Algorithms</strong></p> <ul> <li>Module 00: Promo</li><li>Module 01: Introduction</li><li>Module 02: Common Problem</li><li>Module 03: Depth First Search</li><li>Module 04: Breadth First Search</li><li>Module 05: Breadth First Search Shortest Path on a Grid</li></ul> <p>And much more...</p> <p><strong> Course 14: Data Protection and Data Security Level 2</strong></p> <ul> <li>GDPR Basics</li><li>GDPR Explained</li><li>Lawful Basis for Preparation</li><li>Rights and Breaches</li><li>Responsibilities and Obligations</li></ul><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                             <p>Anyone from any background can enrol in this Data Analysis and Data Science bundle.</p> 
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                             <p>Our Data Analysis and Data Science course is fully compatible with PCs, Macs, laptops, tablets and Smartphone devices.</p> 
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <p>Having this Data Analysis and Data Science expertise will increase the value of your CV and open you up to multiple job sectors.</p> 
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div> <p>You will get the PDF Certificate for the title course (<strong>Introduction to Data Analysis</strong>) absolutely Free! </p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div> <p>You will get the Hard Copy certificate for the title course (<strong>Introduction to Data Analysis</strong>) absolutely Free! Other Hard Copy certificates are available for £10 each. </p> <p>Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.</p> </div>

                        </div>

Apex Learning

Data Analytics with Tableau

<p>Imagine extracting meaningful insights from vast amounts of data with just a few lines of code. Imagine visualising complex data sets and presenting them in an understandable format to your colleagues and clients. That's the power of <strong>R Programming for Data Science</strong>.</p><p>R Programming is essential for data scientists, statisticians, and researchers. This programming language lets you manipulate, analyse, and visualise data quickly. It's an open-source language that has gained widespread popularity due to its flexibility, robustness, and statistical capabilities.</p><p>With our course, you will learn the fundamental concepts of <strong>R Programming</strong> and how to use it for data analysis, visualisation, and modelling. You will learn to manipulate data frames, clean and preprocess data, and build predictive models using machine learning algorithms.</p><p>This course is endorsed by the <strong>Quality Licence Scheme</strong>, which means you can be sure that the content is of the highest quality. Learners will receive a <strong>Level 5 Endorsed</strong> certificate hardcopy upon course completion, adding valuable credentials to their resumes.</p><p>And that's not all. We're also giving away five career-guided courses for free with this <strong>QLS </strong>course. You can choose from our Career Development Plan Fundamentals, Networking Skills for Personal Success, Boost Your Confidence and Self-Esteem, CV Writing and Job Searching, or Learn to Reduce and Manage Stress courses.</p><p>So why wait? Sign up for our R Programming for Data Science course today and unlock the power of data analysis with R programming.</p><p><strong>This Bundle Comprises The Following Courses:</strong></p><p><strong>QLS Endorsed Course:</strong></p><ul><li><strong>Course 01:</strong> Diploma in R Programming for Data Science at QLS Level 5</li></ul><p><strong>CPD QS Accredited Courses:</strong></p><ul><li><strong>Course 01:</strong> Career Development Plan Fundamentals</li><li><strong>Course 02:</strong> Networking Skills for Personal Success</li><li><strong>Course 03:</strong> Boost Your Confidence and Self-Esteem</li><li><strong>Course 04:</strong> CV Writing and Job Searching</li><li><strong>Course 05:</strong> Learn to Reduce and Manage Stress</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Understand the fundamentals of data science and the R programming language.</li><li>Familiarise with RStudio and the R programming environment.</li><li>Comprehend basic data types such as vectors, matrices, and factors and their usage in data manipulation and analysis.</li><li>Master data frames, lists, relational operators, and logical operators and their usage in data manipulation and analysis.</li><li>Develop proficiency in conditional statements, loops, and functions and their usage in data manipulation and analysis.</li><li>Learn and use R packages, apply family functions, regular expressions, dates and times, and data manipulation using the Dplyr package.</li><li>Create and plot data visualisations in R.</li></ul><p><strong>R Programming for Data Science Level 3, 4 &amp; 5</strong><strong>: </strong></p><p>This course is designed to provide individuals with a comprehensive understanding of data science and R programming. The course includes three levels: Level 3, Level 4, and Level 5.</p><p>Level 3 Curriculum provides an overview of data science and introduces students to R programming language. This level consists of six units: Data Science Overview, R and RStudio, Introduction to Basics, Vectors, Matrices, and Factors.</p><p>Level 4 Curriculum focuses on data manipulation and analysis using R. This level consists of seven units: Data Frames, Lists, Relational Operators, Logical Operators, Conditional Statements, Loops, and Functions.</p><p>Level 5 Curriculum focuses on advanced topics in data manipulation and analysis using R.</p><p><strong>Certificate</strong></p><ul><li>In pursuing this QLS-endorsed course, learners attain a profound understanding and amplify their professional prowess, enriching their curriculum vitae.</li><li>The Quality Licence Scheme stands as a reputable and distinguished endorsement, emblematic of exceptional standards and excellence throughout the UK.</li><li>Upon completion, learners will receive a Diploma in R Programming for Data Science at QLS Level 5 Endorsed hard copy certificate, making it easy for them to showcase their achievements.</li></ul><p>Please Note: Imperial Academy is One Education approved resale partner for Quality Licence Scheme Endorsed courses.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        15 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 course is suitable for the following:</p><ul><li>Students and professionals who want to learn data science and R programming language.</li><li>Professionals who want to upskill themselves for better career opportunities.</li><li>Data analysts who want to enhance their data manipulation and analysis approaches using R.</li><li>Programmers who want to learn data science.</li><li>Entrepreneurs who want to learn data science to improve their business decisions.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Anyone can enrol in this R Programming for Data Science course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst - £22k-£51k per year</li><li>Data Scientist - £29k-£72k per year</li><li>Business Intelligence Analyst - £27k-£57k per year</li><li>Machine Learning Engineer - £36k-£98k per year</li><li>Data Engineer - £30k-£75k per year</li><li>Database Administrator - £25k-£60k per year</li><li>Statistician - £25k-£65k per year</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Imperial Academy

Level 5 QLS Endorsed Course | Endorsed Certificate Included | Plus 5 Career Guided Courses | CPD Accredited

R Programming for Data Science Level 3, 4 & 5

<h2>Description</h2><p><span><strong>Robotics Fundamentals Diploma</strong></span></p><p><span>Introducing the Robotics Fundamentals Diploma, an innovative online course tailored for those keen on learning the nuances of modern robotics. This programme is an unparalleled journey into the captivating world of machines, sensors, and algorithms that are shaping the future.</span></p><p><span>Begin with a comprehensive introduction to robotics, setting a solid foundation for your studies. Grasping the basics is paramount, and this diploma ensures learners obtain a deep understanding of these core principles. Next, the course navigates the broader expanse of robotics fundamentals. Here, individuals grasp the intricacies that lie at the heart of these machines.</span></p><p><span>Every robot, from the simplest to the most complex, relies on sensors and actuators. Delving into this topic, this diploma teaches students about their pivotal role in robotics. From sensing external stimuli to initiating precise movements, these components are the nerve centres of any robotic system.</span></p><p><span>The beauty of a robot lies in its movements. Thus, the segments on mechanisms and kinematics elucidate the physical aspects that contribute to their agile motion. Pair this with a detailed analysis of the dynamics and control of robots, and one gets a holistic view of how these machines function and interact with their surroundings.</span></p><p><span>But, how do robots determine their course of action? That's where the module on path planning and navigation comes into play. It allows learners to comprehend how robots chart their routes and avoid obstacles, ensuring smooth and efficient operations.</span></p><p><span>Manipulation and grasping are fundamental robotic tasks, especially in industrial settings. This diploma elaborates on the strategies and techniques robots use to handle objects, ensuring they can efficiently interact with the physical world around them.</span></p><p><span>As we delve further into the modern realm of robotics, the role of computer vision becomes unmistakably evident. This course unravels its significance, showcasing how robots perceive and interpret visual information, ensuring accurate responses to their environment.</span></p><p><span>In today's age, no study of robotics is complete without addressing artificial intelligence and machine learning. These are the cognitive components that arm robots with 'thinking' capabilities. This module of the Robotics Fundamentals Diploma delves deep into the integration of these technologies in robotics, offering insights into how machines learn, adapt, and make decisions.</span></p><p><span>Lastly, as with any forward-looking course, the programme concludes with a session on emerging trends and applications of robotics. From healthcare to space exploration, learners are acquainted with the innovative ways robots are being employed to redefine the boundaries of possibility.</span></p><p><span>In summary, the Robotics Fundamentals Diploma offers a comprehensive online learning experience. Whether you're a budding engineer, a tech enthusiast, or simply someone curious about the robotic world, this course promises to equip you with the knowledge and skills necessary to navigate this ever-evolving landscape. Sign up today and embark on an enlightening journey into the realm of robotics.</span></p><p><span><strong>What you will learn</strong></span></p><p><span><strong>1</strong>:Introduction to Robotics</span><br />
<span><strong>2</strong>:Robotics Fundamentals</span><br />
<span><strong>3</strong>:Sensors and Actuators in Robotics </span><br />
<span><strong>4</strong>:Mechanisms and Kinematics </span><br />
<span><strong>5</strong>:Dynamics and Control of Robots </span><br />
<span><strong>6</strong>:Path Planning and Navigation </span><br />
<span><strong>7</strong>:Manipulation and Grasping </span><br />
<span><strong>8</strong>:Computer Vision in Robotics </span><br />
<span><strong>9</strong>:Artificial Intelligence and Machine Learning in Robotics </span><br />
<span><strong>10</strong>:Emerging Trends and Applications of Robotics</span></p><h4>Course Outcomes</h4><p>
After completing the course, you will receive a diploma certificate and an academic transcript from Elearn college.</p><h4>Assessment</h4><p>
Each unit concludes with a multiple-choice examination. This exercise will help you recall the major aspects covered in the unit and help you ensure that you have not missed anything important in the unit. The results are readily available, which will help you see your mistakes and look at the topic once again. If the result is satisfactory, it is a green light for you to proceed to the next chapter.</p><h4>Accreditation</h4><p>
Elearn College is a registered Ed-tech company under the UK Register of Learning( Ref No:10062668). After completing a course, you will be able to download the certificate and the transcript of the course from the website. For the learners who require a hard copy of the certificate and transcript, we will post it for them for an additional charge.</p>

Elearncollege

Robotics Fundamentals Course

<h2>Description</h2><p><span><b>Fundamentals of Artificial Intelligence diploma</b> </span></p><p><span>Artificial intelligence is widely popular and fascinates most people, especially when compared to technology-related topics. The reason behind it may be that it often goes and wanders beyond what we can imagine. We live in a world where we constantly hold smartphones capable of performing tasks we could never have even thought of just ten years ago. We use technological innovations every day, like voice commands, facial recognition, and other tools that can isolate portraits or take group photos. These are things we never imagined were possible, but we have grown used to them today. </span><br />
<span>We have experienced the wonders of AI, and every new aspect awes us. Although technology has become a part of our daily lives, most do not know how it works. Looking at how amazing the effects of AI are, there is no doubt that the way it works will awe us just as much. Narrow AI is more common, which is AI meant to be used powerfully in one case, like AI for facial recognition or detection of spam. AI systems can be used to sort out vast piles of data like recommendation systems and search engines. They can also provide insights into the data. With this kind of narrow AI, it is expected that it can only perform what it is designed to do. A program meant to detect spam or recognize facial features cannot also be expected to play chess, compose songs, and record shows to watch in the future. </span><br />
<span>The Fundamentals of Artificial Intelligence diploma course aims to remove AI's mystery so that the learners can develop a better in-depth understanding of the technology. The Fundamentals of Artificial Intelligence diploma course consists of an introduction that includes all the basic concepts in AI that will enable you to see what things are possible and what are not within the technology. It will also help you understand the effects of AI on our daily lives. After completing the Fundamentals of Artificial Intelligence diploma course, you will be able to define AI, discuss it, evaluate AI claims, explain the technologies that underpin AI, such as neural networks and machine learning, and understand the main implications of AI. </span><br />
<span>The Fundamentals of Artificial Intelligence diploma course will also discuss AI's concerns and issues like biases, ethics, and jobs. You will also get expert advice regarding learning about AI and beginning a profession in AI-related fields. </span><br />
<span>The fundamentals of Artificial Intelligence diploma does not demand that you have any expertise in computer science or programming. It is simply meant to introduce to you the essentials and basics in the field, regardless of whether you possess a technical background. We aim to encourage as many people as possible from all backgrounds to learn about artificial intelligence, what it is, what it can or cannot do, and how one can start creating methods of AI. The course is flexible and can be studied and completed at whatever pace you are comfortable with. </span></p><p><span><b>What you will learn </b> </span></p><p><span><b>1: </b>An introduction to AI and data </span></p><p><span><b>2: </b>Algorithms and Hardware </span></p><p><span><b>3</b><strong>:</strong> Uses of AI in computer applications and common processes </span></p><p><span><b>4: </b>Using AI for medical needs </span></p><p><span><b>5: </b>AI helping improve human interactions </span></p><p><span><b>6: </b>Data analysis for AI </span></p><p><span><b>7</b><strong>:</strong> Machine learning and deep learning in AI </span></p><p><span><b>8</b><strong>:</strong> AI in hardware applications </span></p><p><span><b>9: </b>AI as a nonstarter </span></p><p><span><b>10</b><strong>:</strong> AI in outer space </span></p><h4>Course Outcomes</h4><p>
After completing the course, you will receive a diploma certificate and an academic transcript from Elearn college.</p><h4>Assessment</h4><p>
Each unit concludes with a multiple-choice examination. This exercise will help you recall the major aspects covered in the unit and help you ensure that you have not missed anything important in the unit. The results are readily available, which will help you see your mistakes and look at the topic once again. If the result is satisfactory, it is a green light for you to proceed to the next chapter.</p><h4>Accreditation</h4><p>
Elearn College is a registered Ed-tech company under the UK Register of Learning( Ref No:10062668). After completing a course, you will be able to download the certificate and the transcript of the course from the website. For the learners who require a hard copy of the certificate and transcript, we will post it for them for an additional charge.</p>

Fundamentals Of Artificial Intelligence Course

<h3><b>Overview</b></h3><p><span>With the ever-increasing demand for </span><span>Data Analysis Level 3 Diploma</span><span> in personal &amp; professional settings, this online training aims at educating, nurturing, and upskilling individuals to stay ahead of the curve - whatever their level of expertise in Data Analysis Level 3 Diploma  may be.</span></p><p><span>Learning about Data Analysis Level 3 Diploma  or keeping up to date on it can be confusing at times, and maybe even daunting! But that's not the case with this course from Compete High. We understand the different requirements coming with a wide variety of demographics looking to get skilled in Data Analysis Level 3 Diploma . That's why we've developed this online training in a way that caters to learners with different goals in mind. The course materials are prepared with consultation from the experts of this field and all the information on Data Analysis Level 3 Diploma  is kept up to date on a regular basis so that learners don't get left behind on the current trends/updates.</span></p><p><span>The self-paced online learning methodology by compete high in this Data Analysis Level 3 Diploma  course helps you learn whenever or however you wish, keeping in mind the busy schedule or possible inconveniences that come with physical classes. The easy-to-grasp, bite-sized lessons are proven to be most effective in memorising and learning the lessons by heart. On top of that, you have the opportunity to receive a certificate after successfully completing the course!</span></p><p><span>Instead of searching for hours, enrol right away on this Data Analysis Level 3 Diploma  course from Compete High and accelerate your career in the right path with expert-outlined lessons and a guarantee of success in the long run.</span></p><h3></h3><h3><b>Who is this course for?</b></h3><p><span>While we refrain from discouraging anyone wanting to do this Data Analysis Level 3 Diploma  course or impose any sort of restrictions on doing this online training, people meeting any of the following criteria will benefit the most from it:</span></p><ol><li><span>Anyone looking for the basics of Data Analysis Level 3 Diploma ,</span></li><li><span>Jobseekers in the relevant domains,</span></li><li><span>Anyone with a ground knowledge/intermediate expertise in Data Analysis Level 3 Diploma ,</span></li><li><span>Anyone looking for a certificate of completion on doing an online training on this topic,</span></li><li><span>Students of Data Analysis Level 3 Diploma , or anyone with an academic knowledge gap to bridge,</span></li><li><span>Anyone with a general interest/curiosity</span></li></ol><h3></h3><h3><b>Career Path</b></h3><p><span>This Data Analysis Level 3 Diploma  course smoothens the way up your career ladder with all the relevant information, skills, and online certificate of achievements. After successfully completing the course, you can expect to move one significant step closer to achieving your professional goals - whether it's securing that job you desire, getting the promotion you deserve, or setting up that business of your dreams.</span></p><p><span> </span></p><p> </p><div><div><div><h3>
Course Curriculum</h3><div><div><h4>
Module 1 Introduction to Data Analysis.</h4><div><div><ul><li><div>
<span></span><h5>
Introduction to Data Analysis.</h5></div><div>
<span>
00:00													</span>
<span></span></div></li></ul></div></div></div><div><h4>
Module 2 Mathematics and Statistics.</h4><div><div><ul><li><div>
<span></span><h5>
Mathematics and Statistics.</h5></div><div>
<span>
00:00													</span>
<span></span></div></li></ul></div></div></div><div><h4>
Module 3 Data Manipulation.</h4><div><div><ul><li><div>
<span></span><h5>
Data Manipulation.</h5></div><div>
<span>
00:00													</span>
<span></span></div></li></ul></div></div></div><div><h4>
Module 4 Data Visualisation.</h4><div><div><ul><li><div>
<span></span><h5>
Data Visualisation.</h5></div><div>
<span>
00:00													</span>
<span></span></div></li></ul></div></div></div><div><h4>
Module 5 Data Wrangling.</h4><div><div><ul><li><div>
<span></span><h5>
Data Wrangling.</h5></div><div>
<span>
00:00													</span>
<span></span></div></li></ul></div></div></div><div><h4>
Module 6 Data Exploration.</h4><div><div><ul><li><div>
<span></span><h5>
Data Exploration.</h5></div><div>
<span>
00:00													</span>
<span></span></div></li></ul></div></div></div><div><h4>
Module 7 Machine Learning Fundamentals.</h4><div><div><ul><li><div>
<span></span><h5>
Machine Learning Fundamentals.</h5></div><div>
<span>
00:00													</span>
<span></span></div></li></ul></div></div></div><div><h4>
Module 8 Machine Learning Algorithms.</h4><div><div><ul><li><div>
<span></span><h5>
Machine Learning Algorithms.</h5></div><div>
<span>
00:00													</span>
<span></span></div></li></ul></div></div></div><div><h4>
Module 9 Data Analysis with Python and Libraries.</h4><div><div><ul><li><div>
<span></span><h5>
Data Analysis with Python and Libraries.</h5></div><div>
<span>
00:00													</span>
<span></span></div></li></ul></div></div></div><div><h4>
Module 10 Data Analysis with R and Libraries.</h4><div><div><ul><li><div>
<span></span><h5>
Data Analysis with R and Libraries.</h5></div><div>
<span>
00:00													</span>
<span></span></div></li></ul></div></div></div></div></div></div></div>

Compete High

Data Analysis Level 3 Diploma

<p>Are you ready to take your data analysis skills to the next level? Introducing the Statistical Analysis and Data Science bundle - the ultimate collection of courses for anyone looking to dive deeper into the world of data.</p><p>The bundle features a QLS-endorsed course, which means you will receive a QLS hardcopy certificate upon completion. This certificate is a mark of quality and can help you stand out in a competitive job market. But that's not all - the bundle also includes 10 other relevant courses, all CPD-QS accredited, to ensure you have a comprehensive understanding of statistical analysis and data science.</p><p>You'll learn everything from the basics of statistical analysis to advanced SAS programming and big data analytics. Our courses were designed by people who are passionate about sharing their knowledge with you. With our easy-to-follow modules, you'll be able to learn at your own pace and from the comfort of your own home.</p><p>Whether you're a seasoned data analyst looking to expand your skills or a newcomer to the field, the Statistical Analysis and Data Science bundle has everything you need to succeed. So why wait? Enrol now and take the first step towards becoming a data analysis expert!</p><p><strong>This Diploma in Statistical Analysis at QLS Level 5</strong><strong> Bundle Package includes:</strong></p><ul><li><strong>Course 01:</strong> Diploma in Statistical Analysis at QLS Level 5</li></ul><p><strong>10 Premium Additional CPD QS Accredited Courses -</strong></p><ul><li><strong>Course 01: </strong>Data Analytics with Tableau</li><li><strong>Course 02: </strong>Big Data Analytics with PySpark Tableau Desktop and MongoDB</li><li><strong>Course 03: </strong>Data Science &amp; Machine Learning with R Training</li><li><strong>Course 04: </strong>SQL for Data Science, Data Analytics and Data Visualization</li><li><strong>Course 05: </strong>Advanced SAS Programming Using MacrosSQL</li><li><strong>Course 06: </strong>SQL NoSQL Big Data and Hadoop</li><li><strong>Course 07: </strong>Statistical Concepts and Application with R</li><li><strong>Course 08: </strong>Business Data Analysis</li><li><strong>Course 09: </strong>Business Intelligence and Data Mining Diploma</li><li><strong>Course 10: </strong>Data Analysis In Excel</li></ul><p><strong>Why Prefer This </strong><strong>Statistical Analysis and Data Science</strong><strong> Bundle?</strong></p><ul><li>You will receive a completely free certificate from the Quality Licence Scheme</li><li>Option to purchase 10 additional certificates accredited by CPD</li><li>Get a free Student ID Card - (£10 postal charges will be applicable for international delivery)</li><li>Free assessments and immediate success results</li><li>24/7 Tutor Support</li></ul> <p><strong>After taking this </strong><strong>Statistical Analysis and Data Science</strong><strong> bundle courses, you will be able to learn:</strong></p><ul><li>Develop a comprehensive understanding of statistical analysis and data science principles</li><li>Gain expertise in data analytics tools such as Tableau, PySpark, MongoDB, R, SQL, SAS, and Hadoop</li><li>Learn advanced data science techniques, including machine learning, data mining, and business intelligence</li><li>Acquire skills in data visualisation, data cleansing, and data analysis in Excel</li><li>Apply statistical concepts and methods to real-world scenarios</li><li>Build a strong foundation in data-driven decision-making</li><li>Develop problem-solving skills and learn how to make data-driven decisions</li></ul><p><strong>***Curriculum breakdown of Statistical Analysis</strong><strong>***</strong></p><ul><li>Module 01: The Realm of Statistics</li><li>Module 02: Basic Statistical Terms</li><li>Module 03: The Center of the Data</li><li>Module 04: Data Variability</li><li>Module 05: Binomial and Normal Distributions</li><li>Module 06: Introduction to Probability</li><li>Module 07: Estimates and Intervals</li><li>Module 08: Hypothesis Testing</li><li>Module 09: Regression Analysis</li><li>Module 10: Algorithms, Analytics and Predictions</li><li>Module 11: Learning From Experience: The Bayesian Way</li><li>Module 12: Doing Statistics: The Wrong Way</li><li>Module 13: How We Can Do Statistics Better</li></ul><p><strong>How is the </strong><strong>Statistical Analysis and Data Science</strong><strong>Bundle Assessment Process?</strong></p><p>You have to complete the assignment questions given at the end of the course and score a minimum of 60% to pass each exam. Our expert trainers will assess your assignment and give you feedback after you submit the assignment.</p><p>You will be entitled to claim a certificate endorsed by the Quality Licence Scheme after you successfully pass the exams.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        250 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">
                            <ul><li>Professionals looking to expand their skills in data analysis</li><li>Students interested in a career in data science and analytics</li><li>Entrepreneurs looking to make data-driven decisions</li><li>Anyone interested in learning statistical analysis and data science principles</li></ul>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Our courses will prepare you for a range of careers, including:</p><ul><li>Data Analyst: £25,000 - £40,000</li><li>Business Analyst: £30,000 - £50,000</li><li>Data Scientist: £40,000 - £70,000</li><li>Business Intelligence Analyst: £35,000 - £55,000</li><li>Big Data Engineer: £50,000 - £80,000</li><li>Data Warehouse Architect: £60,000 - £100,000</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD QS Accredited Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p>Upon successfully completing the Bundle, you will need to place an order to receive a PDF Certificate for each course within the bundle. These certificates serve as proof of your newly acquired skills, accredited by CPD QS. Also, the certificates are recognised throughout the UK and internationally.</p></div>
        <h4>CPD QS Accredited Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>International students are subject to a £10 delivery fee for their orders, based on their location.</p></div>
        <h4>Diploma in Statistical Analysis at QLS Level 5</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Academy for Health and Fitness

Statistical Analysis and Data Science

<p><strong>If Elif Else &gt;&gt; Iterable &gt;&gt;For Loops &gt;&gt; Execute &gt;&gt; Function Objects &gt;&gt; Lambda &gt;&gt; Generators &gt;&gt; Closures &gt;&gt; Classes &gt;&gt; Object Initialisation  and 20+more topics covered.</strong></p><p>Have you been searching for a programming language that is both beginner-friendly and versatile enough for advanced applications? Look no further than Python, the language that powers everything from machine learning algorithms to popular web frameworks like Django and Flask.</p><p>Our online Python course will take you through everything you need to know to start with Python, including how to install the language, navigate the documentation, and create your programs. In addition, you'll learn about essential concepts like variables, syntax, and how to import modules to make your coding experience even smoother.</p><p>But we won't stop there - we'll also dive into more advanced topics like program structure, working with data, functions, and classes and objects. For example, you'll learn how to handle exceptions, work with files and data types like strings, lists, dictionaries, and sets, and create functions with default arguments and doc strings.</p><p>Whether you're just starting or looking to expand your programming skills, our Python online course is the perfect way to get started. So why wait?</p><p>Enrol today and discover the power of Python for yourself!</p><p><strong>Package includes:</strong></p><p><strong>8 Courses: </strong></p><ul><li><strong>Course 01:</strong> Python Programming: Beginner To Expert</li><li><strong>Course 02:</strong> Develop Smart Maps with Python and Leaflet - Windows Version</li><li><strong>Course 03:</strong> Creating REST APIs with Python Django</li><li><strong>Course 04:</strong> Python Accelerator : Mastering Python with Google Bard AI</li><li><strong>Course 05:</strong> Making Automated Trading Bot Using Python</li><li><strong>Course 06:</strong> Git and GitHub Training</li><li><strong>Course 07:</strong> Docker for .Net and Angular Developers</li><li><strong>Course 08:</strong> Web Services Essentials - HTML, Javascript Restful API</li></ul><p><strong>Learning Outcomes:</strong></p><ul><li>Learn Python basics.</li><li>Discover Python's data science capabilities.</li><li>Use Python to accomplish machine learning as well.</li><li>Develop your programming skills by learning Python thoroughly.</li><li>Learn Python so you can launch a successful programming career.</li><li>Become familiar with writing Python loops and decision statements.</li><li>Get knowledge in writing Python function objects.</li><li>Learn Python's file reading and writing syntax.</li><li>Gain knowledge of using traditional inheritance for reusability in Python.</li><li>Learn things of Python application exception handling for handling errors.</li></ul><p><strong>Reality Check: </strong></p><p>Many students remark that despite the course being shorter than others they have taken, it has taught them far more. Many other offline courses that cost five to ten times as much as this one are equally good. Therefore, everyone highly recommends enrolling in this excellent course.</p><p><strong>Why would someone use Python?</strong></p><p>Python and data science is used in every aspect of life, including software for computers, websites, and mobile applications. Python is fantastic for practically any development demand, including servers, operating system, software, game, and programming. This course will teach you Python in a hands-on way, and each lecture is accompanied by a complete screencast of coding and an associated code notebook!</p><p><strong>Learn in the way that works best for you!</strong></p><p><strong>Curriculum Breakdown: </strong></p><ul><li>Module 01: Introduction To Python Programming From A-Z</li><li>Module 02: Getting Familiar With Python</li><li>Module 03: Basic Data Types</li><li>Module 04: Python Operators</li><li>Module 05: Advanced Data Types</li><li>Module 06: Control Flow Part 1</li><li>Module 07: Control Flow Part 2</li><li>Module 08: Python Functions</li><li>Module 09: User Input And Error Handling</li><li>Module 10: Python Advanced Functions</li><li>Module 11: Python Scripting And Libraries</li><li>Module 12: NumPy</li><li>Module 13: Pandas</li><li>Module 14: Introduction To OOP</li><li>Module 15: Advanced OOP</li><li>Module 16: Starting A Career In Python</li></ul><p>Additionally, you will receive additional informative modules from bonus courses.</p><p><strong>Method of Assessment</strong></p><ul><li>MCQ Based</li><li>Passing Marks 60%</li><li>Unlimited Retake Exam</li><li>Instant Assessment and Feedback</li></ul><p><strong>Certification</strong></p><ul><li>Quality Licence Scheme (QLS) Endorsed Certificate</li><li><strong>CPD Accredited (CPD QS) Certificate </strong></li><li>Digital (PDF) &amp; Hardcopy certificates are available</li></ul><h2>
        
        <div>CPD</div>
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                                        260 CPD hours / points
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                                            Accredited by CPD Quality Standards
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        <div>Who is this course for?</div>
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                            <p>This course is ideal for-</p><ul><li>Beginners with no previous programming experience.</li><li>Programming language switchers among programmers.</li><li>Programmers looking to advance their careers!</li><li>Anyone interested in entering the hot job markets for: web development, machine learning, data science, etc.</li></ul><p>Please note, This course will only provide you with the theoretical foundation you need to improve in this particular area. To get qualified, you need to enrol in one of the following programmes related to Python at QLS Level 3:</p><ul><li>BTEC Level 3 National Diploma in Computing</li><li>BTEC Level 3 National Extended Diploma in Computing</li><li>OCR Level 3 Cambridge Technical Diploma in IT</li><li>OCR Level 3 Cambridge Technical Extended Diploma in IT</li><li>T Level in Digital Production, Design and Development</li><li>Foundation Degree in Computer Science</li><li>Bachelor's Degree in Computer Science</li><li>Master's Degree in Computer Science</li><li>Apprenticeship in Software Development</li><li>Apprenticeship in Data Analysis.</li></ul>
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        <div>Requirements</div>
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                            <p>There are no formal entry requirements for python bundle, with enrolment open to anyone!</p><p>Learners seeking to enrol for the course should meet the following requirements;</p><ul><li>Basic knowledge of the English language, which you have already</li><li>Basic Knowledge of Information &amp; Communication Technologies for studying online or digital platform</li><li>Stable Internet or Data connection in your learning devices</li></ul>
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        <div>Career path</div>
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                        <div id="career_path">
                            <p>Studying the Python Programming bundle is designed to help you get the job of your dreams or even that promotion you've been waiting for a long time.</p><p>You can explore jobs like-</p><ul><li>Python Developer</li><li>Data Scientist</li><li>Full Stack Developer</li><li>Python Software Engineer</li><li>Backend Developer</li><li>Automation Engineer</li><li>Django Developer</li><li>Python Data Analyst</li><li>Machine Learning Engineer</li><li>Python Consultant</li></ul>
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        <div>Certificates</div>
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                                    <h4> Digital certificate</h4>
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            Digital certificate -
            £9
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        <h4> Hard copy certificate</h4>
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            Hard copy certificate -
            £15
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        <h4>QLS Endorsed Certificate</h4>
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            Hard copy certificate -
            £129
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                        </div>

One Education

Python for Beginner, Intermediate & Advanced - QLS Endorsed Level 7 Course

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud.</p><p></p>Overview<p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure.</p><p></p><p>Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow.</p><p></p>Prerequisites<p></p><li>Creating cloud resources in Microsoft Azure.</li>  <li>Using Python to explore and visualize data.</li>  <li>Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.</li>  <li>Working with containers</li>  <li>AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.</li><p></p></div><div id="course-pills-2"><h3>1 - Design a data ingestion strategy for machine learning projects</h3> <ul> <li>Identify your data source and format</li> <li>Choose how to serve data to machine learning workflows</li> <li>Design a data ingestion solution</li> </ul> <h3>2 - Design a machine learning model training solution</h3> <ul> <li>Identify machine learning tasks</li> <li>Choose a service to train a machine learning model</li> <li>Decide between compute options</li> </ul> <h3>3 - Design a model deployment solution</h3> <ul> <li>Understand how model will be consumed</li> <li>Decide on real-time or batch deployment</li> </ul> <h3>4 - Design a machine learning operations solution</h3> <ul> <li>Explore an MLOps architecture</li> <li>Design for monitoring</li> <li>Design for retraining</li> </ul> <h3>5 - Explore Azure Machine Learning workspace resources and assets</h3> <ul> <li>Create an Azure Machine Learning workspace</li> <li>Identify Azure Machine Learning resources</li> <li>Identify Azure Machine Learning assets</li> <li>Train models in the workspace</li> </ul> <h3>6 - Explore developer tools for workspace interaction</h3> <ul> <li>Explore the studio</li> <li>Explore the Python SDK</li> <li>Explore the CLI</li> </ul> <h3>7 - Make data available in Azure Machine Learning</h3> <ul> <li>Understand URIs</li> <li>Create a datastore</li> <li>Create a data asset</li> </ul> <h3>8 - Work with compute targets in Azure Machine Learning</h3> <ul> <li>Choose the appropriate compute target</li> <li>Create and use a compute instance</li> <li>Create and use a compute cluster</li> </ul> <h3>9 - Work with environments in Azure Machine Learning</h3> <ul> <li>Understand environments</li> <li>Explore and use curated environments</li> <li>Create and use custom environments</li> </ul> <h3>10 - Find the best classification model with Automated Machine Learning</h3> <ul> <li>Preprocess data and configure featurization</li> <li>Run an Automated Machine Learning experiment</li> <li>Evaluate and compare models</li> </ul> <h3>11 - Track model training in Jupyter notebooks with MLflow</h3> <ul> <li>Configure MLflow for model tracking in notebooks</li> <li>Train and track models in notebooks</li> </ul> <h3>12 - Run a training script as a command job in Azure Machine Learning</h3> <ul> <li>Convert a notebook to a script</li> <li>Run a script as a command job</li> <li>Use parameters in a command job</li> </ul> <h3>13 - Track model training with MLflow in jobs</h3> <ul> <li>Track metrics with MLflow</li> <li>View metrics and evaluate models</li> </ul> <h3>14 - Perform hyperparameter tuning with Azure Machine Learning</h3> <ul> <li>Define a search space</li> <li>Configure a sampling method</li> <li>Configure early termination</li> <li>Use a sweep job for hyperparameter tuning</li> </ul> <h3>15 - Run pipelines in Azure Machine Learning</h3> <ul> <li>Create components</li> <li>Create a pipeline</li> <li>Run a pipeline job</li> </ul> <h3>16 - Register an MLflow model in Azure Machine Learning</h3> <ul> <li>Log models with MLflow</li> <li>Understand the MLflow model format</li> <li>Register an MLflow model</li> </ul> <h3>17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning</h3> <ul> <li>Understand Responsible AI</li> <li>Create the Responsible AI dashboard</li> <li>Evaluate the Responsible AI dashboard</li> </ul> <h3>18 - Deploy a model to a managed online endpoint</h3> <ul> <li>Explore managed online endpoints</li> <li>Deploy your MLflow model to a managed online endpoint</li> <li>Deploy a model to a managed online endpoint</li> <li>Test managed online endpoints</li> </ul> <h3>19 - Deploy a model to a batch endpoint</h3> <ul> <li>Understand and create batch endpoints</li> <li>Deploy your MLflow model to a batch endpoint</li> <li>Deploy a custom model to a batch endpoint</li> <li>Invoke and troubleshoot batch endpoints</li> </ul></div>

Nexus Human

Data Engineering

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

DP-100T01 Designing and Implementing a Data Science Solution on Azure

Data Science

Python

Welcome to the Bootcamp course. You will obtain a firm understanding of machine learning with this course. By doing so, you will be able to develop machine learning solutions for various challenges you might encounter and be prepared to start using machine learning at work or in technical interviews.

Python Machine Learning Bootcamp

Go from Beginner to Super Advance Level in Machine Learning Algorithms using Python and Mathematical Insights

Machine Learning with Real World Projects

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			<h2><b>Course Overview</b></h2>		</div>
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					<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>
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					<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>
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Course Cloud

Machine Learning (ML)

Machine Learning for Apps Level 4

<strong>The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.</strong>
        <p>Machine learning is a field of computer science through which you can create complex models that perform multiple functions using mathematical input. Python is a popular choice to create machine learning models due to a plethora of libraries easily accessible. This course takes you through this impressive combination of Python and machine learning, teaching you the basics of machine learning to create your own projects.

You'll begin learning about different types of machine learning models and how to choose the relevant ones for your project. You'll learn to optimize this model and apply performance metrics to track its performance. You'll also learn topics like regression, classification, and clustering to improve the performance of your model. You'll learn the basics of neural networks and use scikit-learn to perform calculations in your project.

By the end of this course, you'll have created a face recognition application using everything you've learned in this course.

The code bundle for this course is available at https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>What You Will Learn</h2><p>Train different types of machine learning models for your project<br>Prepare and clean data for your project<br>Optimize your machine learning model to best suit your project needs<br>Build your own machine learning model from scratch<br>Apply performance metrics to track the performance of your model<br>Use scikit-learn to perform calculations in your project</p>
        <h2>Audience</h2><p>This course is for Python developers who are new in the field of machine learning. No prior knowledge or experience of machine learning is required. A basic understanding of Python programming will be needed here.</p>
        <h2>Approach</h2><p>This is a hands-on course that takes you through all the machine learning concepts step-by-step while showing you real-world applications for each step.</p>
        <h2>Key Features</h2><p>Build a face recognition application from scratch * Prepare and train your data for your projects * Learn real-world applications of your algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Machine-Learning-Crash-Course-for-Beginners</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

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

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction to the Course</h4><p></p><br><h4>2. Why Machine Learning</h4><p></p><br><h4>3. Process of Learning from Data</h4><p></p><br><h4>4. Machine Learning Methods</h4><p></p><br><h4>5. Data Preparation and Preprocessing</h4><p></p><br><h4>6. Machine Learning Models and Optimization</h4><p></p><br><h4>7. Building Machine Learning Model from Scratch</h4><p></p><br><h4>8. Overfitting, Underfitting and Generalization</h4><p></p><br><h4>9. Machine Learning Model Performance Metrics</h4><p></p><br><h4>10. Dimensionality Reduction</h4><p></p><br><h4>11. Deep Learning Overview</h4><p></p><br><h4>12. Hands-on Machine Learning Project Using Scikit-Learn</h4><p></p><br><h4>13. OPTIONAL Section- Mathematics Wrap-up</h4><p></p>

The goal of this course is to use Python machine learning to create algorithms that you can use in the real world. You'll start with the basics of machine learning. You'll learn how to create, train, and optimize models and use these models in real-world applications.

Python Machine Learning Crash Course for Beginners

<strong>Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis</strong>
        <p>Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. 

In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. 

We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist! 

All the code and supporting files for this course are available at https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels</p>
        <h2>What You Will Learn</h2><p>Confidently work with two of the leading ML packages: statsmodels and sklearn <br>Understand how to perform a linear regression <br>Become familiar with the ins and outs of logistic regression <br>Get to grips with carrying out cluster analysis (both flat and hierarchical) <br>Apply your skills to real-life business cases <br>Get insights into the underlying ideas behind ML models</p>
        <h2>Audience</h2><p>If you want to get acquainted with fundamental machine learning methods, become a successful data scientist, or just get started with building valuable skills in machine learning and data science, this course is for you.</p>
        <h2>Approach</h2><p>The course has a perfect blend of theory and practical knowledge to help you gain a solid understand of the concepts covered.</p>
        <h2>Key Features</h2><p>Learn machine learning with StatsModels and sklearn  * Apply machine learning skills to solve real-world business cases  * Get started with linear regression, logistic regression, and cluster analysis</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/machine-learning-101-with-scikit-learn-and-statsmodels</p>
        <h2>About the Author</h2><h3>365 Careers Ltd.</h3><p>365 Careers' courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers' courses are the perfect place to start.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Setting Up the Working Environment</h4><p></p><br><h4>3. Linear Regression with StatsModels</h4><p></p><br><h4>4. Linear Regression with Sklearn</h4><p></p><br><h4>5. Linear Regression - Practical Example</h4><p></p><br><h4>6. Logistic Regression</h4><p></p><br><h4>7. Cluster Analysis</h4><p></p><br><h4>8. Cluster Analysis: Additional Topics</h4><p></p>

Begin your machine learning journey by learning all about linear regression, logistic regression, and cluster analysis

Machine Learning 101 with Scikit-learn and StatsModels

<strong>In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.</strong>
        <p>Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library.

The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms.

By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects.

The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2</p>
        <h2>What You Will Learn</h2><p>Understand how to use Jupyter Lab for Jupyter notebooks<br>Learn Python fundamentals<br>Load large datasets from files using pandas<br>Uncover techniques to perform data analysis and exploration<br>Group, sort, export, select, filter, and clean data<br>Find out how to preview the data frame</p>
        <h2>Audience</h2><p>This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.</p>
        <h2>Approach</h2><p>With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.</p>
        <h2>Key Features</h2><p>Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 </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 2!</h4><p></p><br><h4>2. Python Fundamentals for Data Science</h4><p></p><br><h4>3. Introduction to the Pandas Library</h4><p></p><br><h4>4. Loading Data into a DataFrame</h4><p></p><br><h4>5. Data Cleaning and Transformation</h4><p></p><br><h4>6. Course Summary</h4><p></p>

In this course, you will learn Python fundamentals, and the concepts of the amazing pandas data science library needed to pre-process and prepare the data for machine learning algorithms.

Machine Learning for Absolute Beginners - Level 2

<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>

TensorFlow

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

The Complete Machine Learning Course with Python

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					<div><p>The comprehensive Machine Learning - Use in Flutter has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Machine Learning - Use in Flutter today, and learn from the very best the industry has to offer!</p></div>
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					<div><p>This best selling Machine Learning - Use in Flutter has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning - Use in Flutter is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Machine Learning - Use in Flutter is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Machine Learning - Use in Flutter 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 Machine Learning - Use in Flutter, 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 Machine Learning - Use in Flutter 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 Machine Learning - Use in Flutter 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>
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Learning & Development

Learning To Learn

Machine Learning - Use in Flutter

<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>

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.

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