Name: Data Analytics with Tableau Diploma
Price: 149 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>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

<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>
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Data Analysis with Python and Libraries.</h5></div><div>
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Module 10 Data Analysis with R and Libraries.</h4><div><div><ul><li><div>
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Compete High

Data Analysis Level 3 Diploma

<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

<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>
        
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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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                        <div id="who_is_this_course_for">
                            <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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                            <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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                                    <h4> Digital certificate</h4>
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            Digital certificate -
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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>
        <p>
            Hard copy certificate -
            £129
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                        </div>

One Education

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

<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>

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                            <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>

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                            <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>
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        <div>Certificates</div>
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                                    <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

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Explore the diverse realm of Big Data Analytics, delving into data mining, machine learning, and natural language processing. Uncover the ethical and legal dimensions, and gain practical insights through real-world case studies.
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<p>Discover types of Big Data analytics, delve into the data mining process, and apply machine learning techniques. Learn about natural language processing principles, assess the impact across industries, and navigate ethical considerations.<br /><br /></p>
<p>After the successful completion of this course, you will be able to:<br /><br /></p>
<ul>
<li>Identify the types of Big Data analytics, such as descriptive, predictive, and prescriptive analytics.</li>
<li>Understand the data mining process and its role in Big Data analytics.</li>
<li>Apply machine learning techniques like classification, regression, and clustering to analyse Big Data.</li>
<li>Describe the principles and applications of natural language processing in Big Data analytics.</li>
<li>Recognize the benefits and limitations of Big Data analytics in different industries, such as healthcare, finance, and marketing.</li>
<li>Evaluate the accuracy and reliability of Big Data analytics results.</li>
<li>Understand Big Data analytics's ethical and legal considerations, such as data privacy and intellectual property rights.</li>
<li>Analyze case studies and examples of successful implementation of Big Data analytics in different industries, such as predicting customer churn, fraud detection, and personalized medicine.</li>
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<div> <div id="headingTwo"> <h2>  </h2> </div> <div id="collapseTwo"> <div> <div> <div> <div> <div> <div id="msbmAccordion"> <div> <div> <div> <div> <div id="headingOne1"> <h2>  </h2> </div> <div id="collapse2898"> <div> <p>Immerse yourself in Big Data Analytics, mastering techniques to extract valuable insights. From predictive modelling to ethical considerations, this course empowers you with the skills to excel in a data-driven landscape, with applications ranging from healthcare to finance.</p> <ul> <li> <div> <div> <i></i> </div> <div> <h5>VIDEO - Course Structure and Assessment Guidelines</h5> <p>Watch this video to gain further insight.</p> </div> </div> </li> <li> <div> <div> <i></i> </div> <div> <h5>Navigating the MSBM Study Portal</h5> <p>Watch this video to gain further insight.</p> </div> </div> </li> <li> <div> <div> <i></i> </div> <div> <h5>Interacting with Lectures/Learning Components</h5> <p>Watch this video to gain further insight.</p> </div> </div> </li> <li> <div> <div> <i></i> </div> <div> <h5>Big Data Analytics, Techniques and Models</h5> <p>Self-paced pre-recorded learning content on this topic.</p> </div> </div> </li> <li> <div> <div> <i></i> </div> <div> <h5>Big Data Analytics</h5> <p>Put your knowledge to the test with this quiz. Read each question carefully and choose the response that you feel is correct. </p> </div> </div> </li> </ul> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
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All MSBM courses are accredited by the relevant partners and awarding bodies. Please refer to MSBM accreditation in about us for more details.
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There are no strict entry requirements for this course. Work experience will be an added advantage to understanding the content of the course.<br />
<p>The certificate is designed to enhance the learner's knowledge in the field. This certificate is for everyone who is eager to know more and get updated on current ideas in their respective field. We recommend this certificate for the following audience.</p>
<ul>
<li>Big Data Analyst</li>
<li>Data Scientist</li>
<li>Machine Learning Engineer</li>
<li>Business Intelligence Analyst</li>
<li>Predictive Analytics Expert</li>
<li>Information Architect</li>
<li>Data Engineer</li>
<li>AI Solutions Architect</li>
</ul>
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<p>2 Weeks</p>
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<p>3 CPD Hours</p>
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<p>Advanced</p>
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<p>Anytime</p>
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<h5>100% Online</h5>
<p>Study online with ease.</p>
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<h5>Unlimited Access</h5>
<p>24/7 unlimited access with pre-recorded lectures.</p>
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<h5>Low Fees</h5>
<p>Our fees are low and easy to pay online.</p>
</div>
</div>
</div>

Metropolitan School of Business & Management UK

Business Analytics & Intelligence

Big Data Analytics

Data Science

Marketing Analytics

Diploma in Management Studies (DMS)

Data Analyst

Professional Certificate Course in Big Data Analytics in London 2024

<p>Are you ready to dive deep into the world of data analytics? Look no further than our Data Analytics with Tableau bundle, featuring 11 comprehensive courses that will give you the skills and knowledge needed to succeed in the world of data analysis.</p><p>Begin with the QLS-endorsed Advanced Diploma in Data Analytics with Tableau at QLS Level 6, and receive a free hardcopy certificate upon completion. This prestigious certification will set you apart from your peers.</p><p>Continue your learning journey with ten additional courses, all CPD-QS accredited, covering everything from SQL Masterclass to GDPR compliance. Our courses are designed to provide a theoretical understanding of data analytics that will serve as a strong foundation for your future career in the field.</p><p>Whether you're just starting out in data analytics or are a seasoned professional looking to expand your knowledge, our Data Analytics with Tableau bundle is the perfect choice for you. Enrol now and take the first step towards a brighter future in the exciting world of data analytics.</p><p><strong>This Data Analytics with Tableau Bundle Package includes:</strong></p><ul><li><strong>Course 01:</strong> Advanced Diploma in Data Analytics with Tableau at QLS Level 6</li></ul><p><strong>10 Premium Additional CPD QS Accredited Courses -</strong></p><ul><li><strong>Course 01:</strong> Data Analysis</li><li><strong>Course 02:</strong> SQL Masterclass: SQL For Data Analytics</li><li><strong>Course 03:</strong> Data Science &amp; Machine Learning with R Training</li><li><strong>Course 04:</strong> Google Data Studio</li><li><strong>Course 05:</strong> Excel Data Analysis</li><li><strong>Course 06:</strong> Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query</li><li><strong>Course 07:</strong> Business Intelligence and Data Mining Diploma</li><li><strong>Course 08:</strong> Business Data Analysis</li><li><strong>Course 09:</strong> Statistical Analysis</li><li><strong>Course 10:</strong> GDPR</li></ul><p><strong>Why Prefer This Data Analytics with Tableau 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 Data Analytics with Tableau</strong><strong> bundle courses, you will be able to learn:</strong></p><ul><li>Gain a thorough understanding of data analytics concepts and tools.</li><li>Master Tableau and Excel for data analysis and visualisation.</li><li>Learn how to work with SQL and relational databases for data management.</li><li>Develop skills in data science and machine learning using R.</li><li>Understand the principles of business intelligence and data mining.</li><li>Gain knowledge of statistical analysis and interpretation of results.</li><li>Acquire a comprehensive understanding of GDPR compliance and regulations.</li></ul><p><strong>***Curriculum breakdown of Data Analytics with Tableau</strong><strong>***</strong></p><ul><li><strong>Module 01:</strong> Introduction to the Course</li><li><strong>Module 02:</strong> Project 1: Discount Mart (Sales and Profit Analytics)</li><li><strong>Module 03: </strong>Project 2: Green Destinations (HR Analytics)</li><li><strong>Module 04:</strong> Project 3: Superstore (Sales Agent Tracker)</li><li><strong>Module 05: </strong>Northwind Trade (Shipping Analytics)</li><li><strong>Module 06: </strong>Project 5: Tesla (Stock Price Analytics)</li><li><strong>Module 07:</strong> Bonus: Introduction to Database Concepts</li><li><strong>Module 08: </strong>Tableau Stories</li></ul><p><strong>How is the Data Analytics with Tableau</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>Individuals interested in pursuing a career in data analytics.</li><li>Professionals seeking to enhance their data analysis skills.</li><li>Business analysts, data scientists, and statisticians.</li><li>Anyone looking to gain a theoretical foundation in data analytics.</li></ul>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £20k-£48k per year</li><li>Business Intelligence Analyst: £24k-£60k per year</li><li>Data Scientist: £33k-£75k per year</li><li>Business Analyst: £24k-£50k per year</li><li>Data Engineer: £28k-£80k per year</li><li>Statistician: £25k-£45k per year</li></ul><p>Note: Salary ranges are subject to change depending on location, experience, and industry.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Advanced Diploma in Data Analytics with Tableau at QLS Level 6</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div> <p>After successfully completing the course, you can order an original hardcopy Certificate of Achievement endorsed by the <strong>Quality Licence Scheme</strong>. This certificate will be delivered to your home, free of charge.</p><p>To obtain the additional Quality License Scheme Endorsed Certificate, you will need to pay the corresponding fee based on the level of the courses:</p><ul><li>Level 1: £59</li><li>Level 2: £79</li><li>Level 3: £89</li><li>Level 4: £99</li><li>Level 5: £119</li><li>Level 6: £129</li><li>Level 7: £139</li></ul> </div>
        <h4>CPD QS Accredited Certificate</h4>
        <p>
            Digital certificate -
            £10
        </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 and will cost you £10 for each course. Also, the certificates are recognised throughout the UK and internationally.</p> </div>
        <h4>CPD QS Accredited Certificate</h4>
        <p>
            Hard copy certificate -
            £29
        </p>
            <div> <p>International students are subject to a £10 delivery fee for their orders, based on their location.</p> </div>

                        </div>

Data Analytics with Tableau Diploma

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p></p><p>The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don?t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful.</p><p></p><p></p><p>This course introduces fundamentals concepts related to artificial intelligence (AI), and the services in Microsoft Azure that can be used to create AI solutions. The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. </p><p></p>Prerequisites<p>Prerequisite certification is not required before taking this course. Successful Azure AI Fundamental students start with some basic awareness of computing and internet concepts, and an interest in using Azure AI services.   Specifically:  </p><ul> <li>Experience using computers and the internet.</li> <li>Interest in use cases for AI applications and machine learning models. </li> <li>A willingness to learn through hands-on exp...</li><p></p></ul></div><div id="course-pills-2"><h3>1 - Fundamental AI Concepts</h3> <ul> <li>Understand machine learning</li> <li>Understand computer vision</li> <li>Understand natural language processing</li> <li>Understand document intelligence and knowledge mining</li> <li>Understand generative AI</li> <li>Challenges and risks with AI</li> <li>Understand Responsible AI</li> </ul> <h3>2 - Fundamentals of machine learning</h3> <ul> <li>What is machine learning?</li> <li>Types of machine learning</li> <li>Regression</li> <li>Binary classification</li> <li>Multiclass classification</li> <li>Clustering</li> <li>Deep learning</li> <li>Azure Machine Learning</li> </ul> <h3>3 - Fundamentals of Azure AI services</h3> <ul> <li>AI services on the Azure platform</li> <li>Create Azure AI service resources</li> <li>Use Azure AI services</li> <li>Understand authentication for Azure AI services</li> </ul> <h3>4 - Fundamentals of Computer Vision</h3> <ul> <li>Images and image processing</li> <li>Machine learning for computer vision</li> <li>Azure AI Vision</li> </ul> <h3>5 - Fundamentals of Facial Recognition</h3> <ul> <li>Understand Face analysis</li> <li>Get started with Face analysis on Azure</li> </ul> <h3>6 - Fundamentals of optical character recognition</h3> <ul> <li>Get started with Vision Studio on Azure</li> </ul> <h3>7 - Fundamentals of Text Analysis with the Language Service</h3> <ul> <li>Understand Text Analytics</li> <li>Get started with text analysis</li> </ul> <h3>8 - Fundamentals of question answering with the Language Service</h3> <ul> <li>Understand question answering</li> <li>Get started with the Language service and Azure Bot Service</li> </ul> <h3>9 - Fundamentals of conversational language understanding</h3> <ul> <li>Describe conversational language understanding</li> <li>Get started with conversational language understanding in Azure</li> </ul> <h3>10 - Fundamentals of Azure AI Speech</h3> <ul> <li>Understand speech recognition and synthesis</li> <li>Get started with speech on Azure</li> </ul> <h3>11 - Fundamentals of Azure AI Document Intelligence</h3> <ul> <li>Explore capabilities of document intelligence</li> <li>Get started with receipt analysis on Azure</li> </ul> <h3>12 - Fundamentals of Knowledge Mining with Azure Cognitive Search</h3> <ul> <li>What is Azure Cognitive Search?</li> <li>Identify elements of a search solution</li> <li>Use a skillset to define an enrichment pipeline</li> <li>Understand indexes</li> <li>Use an indexer to build an index</li> <li>Persist enriched data in a knowledge store</li> <li>Create an index in the Azure portal</li> <li>Query data in an Azure Cognitive Search index</li> </ul> <h3>13 - Fundamentals of Generative AI</h3> <ul> <li>What is generative AI?</li> <li>Large language models</li> <li>What is Azure OpenAI?</li> <li>What are copilots?</li> <li>Improve generative AI responses with prompt engineering</li> </ul> <h3>14 - Fundamentals of Azure OpenAI Service</h3> <ul> <li>What is generative AI</li> <li>Describe Azure OpenAI</li> <li>How to use Azure OpenAI</li> <li>Understand OpenAI's natural language capabilities</li> <li>Understand OpenAI code generation capabilities</li> <li>Understand OpenAI's image generation capabilities</li> <li>Describe Azure OpenAI's access and responsible AI policies</li> </ul> <h3>15 - Fundamentals of Responsible Generative AI</h3> <ul> <li>Plan a responsible generative AI solution</li> <li>Identify potential harms</li> <li>Measure potential harms</li> <li>Mitigate potential harms</li> <li>Operate a responsible generative AI solution</li> </ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans AI-900T00 - Microsoft Azure AI Fundamentals training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the AI-900T00 - Microsoft Azure AI Fundamentals course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

Microsoft AI-900

AI-900T00 Microsoft Azure AI Fundamentals

<p>If you aim to enhance your Machine Learning &amp; Artificial Intelligence skills, our comprehensive Machine Learning &amp; Artificial Intelligence course is perfect for you. Designed for success, this Machine Learning &amp; Artificial Intelligence course covers everything from basics to advanced topics in Machine Learning &amp; Artificial Intelligence.</p><p>Each lesson in this Machine Learning &amp; Artificial Intelligence course is crafted for easy understanding, enabling you to become proficient in Machine Learning &amp; Artificial Intelligence. Whether you are a beginner or looking to sharpen your existing skills, this Machine Learning &amp; Artificial Intelligence is the ideal choice.</p><p>With our Machine Learning &amp; Artificial Intelligence exclusive bundle, you will get<strong> a PDF Certificate, PDF Transcript and Digital Student ID Card (worth £50) Absolutely FREE. </strong></p><p><strong>Courses are Included in This Bundle:</strong></p><ul><li><strong>Course 01: </strong>Machine Learning Basics</li><li><strong>Course 02: </strong>Hands-on Machine Learning Project - Auto Image Captioning for Social Media</li><li><strong>Course 03: </strong>Machine Learning with Python Course</li><li><strong>Course 04: </strong>Project on Deep Learning - Artificial Neural Network</li><li><strong>Course 05: </strong>Data Science &amp; Machine Learning with R Training</li><li><strong>Course 06: </strong>ChatGPT for Marketing and Productivity with AI Tools</li></ul><p><strong>Why Choose Our Course?</strong></p><ul><li>FREE Machine Learning &amp; Artificial Intelligence certificate accredited</li><li>Get a free student ID card with Machine Learning &amp; Artificial Intelligence Training</li><li>Get instant access to this Machine Learning &amp; Artificial Intelligence course.</li><li>Learn Machine Learning &amp; Artificial Intelligence from anywhere in the world</li><li>Machine Learning &amp; Artificial Intelligence is affordable and simple to understand</li><li>This bundle is an entirely online, interactive lesson with voiceover audio</li><li>Lifetime access to the Machine Learning &amp; Artificial Intelligence course materials</li><li>The Machine Learning &amp; Artificial Intelligence comes with 24/7 tutor support</li></ul><p>So enrol now in this Machine Learning &amp; Artificial Intelligence Today to advance your career!</p> <p>Start your learning journey straightaway!</p><p>This Machine Learning &amp; Artificial Intelligence's curriculum has been designed by Machine Learning &amp; Artificial Intelligence experts with years of Machine Learning &amp; Artificial Intelligence experience behind them. The Machine Learning &amp; Artificial Intelligence course is extremely dynamic and well-paced to help you understand Machine Learning &amp; Artificial Intelligence with ease. You'll discover how to master Machine Learning &amp; Artificial Intelligence skills while exploring relevant and essential topics.</p><p><strong>Assessment Process</strong></p><p>Once you have completed all the courses in the Machine Learning &amp; Artificial Intelligence bundle, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards.</p> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>This Machine Learning &amp; Artificial Intelligence bundle is suitable for everyone.</p>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

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

                        <div id="career_path">
                            <p>This Machine Learning &amp; Artificial Intelligence bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited  Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            £29
        </p>
            <div><p>If you are an international student, you will be required to pay an additional fee of <strong>10 GBP</strong> for international delivery, and <strong>4.99 GBP</strong> for delivery within the UK, for each certificate</p></div>

                        </div>

If you aim to enhance your Machine Learning & Artificial Intelligence skills, our comprehensive Machine Learning & Artificial Intelligence course is perfect for you. Designed for success, this Machine Learning & Artificial Intelligence course covers everything from basics to advanced topics in Machine Learning & Artificial Intelligence.
Each lesson in this Machine Learning & Artificial Intelligence course is crafted for easy understanding, enabling you to become proficient in Machine Learning & Artificial Intelligence. Whether you are a beginner or looking to sharpen your existing skills, this Machine Learning & Artificial Intelligence is the ideal choice.
With our Machine Learning & Artificial Intelligence exclusive bundle, you will get a PDF Certificate, PDF Transcript and Digital Student ID Card (worth £50) Absolutely FREE. 
Courses are Included in This Machine Learning & Artificial Intelligence Bundle:
Course 01: Machine Learning Basics
Course 02: Hands-on Machine Learning Project - Auto Image Captioning for Social Media
Course 03: Machine Learning with Python Course
Course 04: Project on Deep Learning - Artificial Neural Network
Course 05: Data Science & Machine Learning with R Training
Course 06: ChatGPT for Marketing and Productivity with AI Tools
Learning Outcomes of this Machine Learning & Artificial Intelligence
Understand the foundational concepts of machine learning and its applications.
Gain practical experience through hands-on machine learning projects and tools.
Master Python programming for effective machine learning applications and tasks.
Develop deep learning models using artificial neural networks effectively.
Apply data science techniques using R for comprehensive data analysis.
Learn to utilise AI tools for marketing and enhancing productivity in business.
Why Choose Our Course?
FREE Machine Learning & Artificial Intelligence certificate accredited
Get a free student ID card with Machine Learning & Artificial Intelligence Training
Get instant access to this Machine Learning & Artificial Intelligence course.
Learn Machine Learning & Artificial Intelligence from anywhere in the world
Machine Learning & Artificial Intelligence is affordable and simple to understand
This bundle is an entirely online, interactive lesson with voiceover audio
Lifetime access to the Machine Learning & Artificial Intelligence course materials
The Machine Learning & Artificial Intelligence comes with 24/7 tutor support
So enrol now in this Machine Learning & Artificial Intelligence Today to advance your career!



Start your learning journey straightaway with Machine Learning & Artificial Intelligence!
This Machine Learning & Artificial Intelligence's curriculum has been designed by Machine Learning & Artificial Intelligence experts with years of Machine Learning & Artificial Intelligence experience behind them. The Machine Learning & Artificial Intelligence course is extremely dynamic and well-paced to help you understand Machine Learning & Artificial Intelligence with ease. You'll discover how to master Machine Learning & Artificial Intelligence skills while exploring relevant and essential topics.
*** Course Curriculum ***
Course 01: Machine Learning Basics
Section 01: Introduction
Section 02: Regression
Section 03: Predictors
Section 04: Minitab
Section 05: Regression Trees
Section 06: Binary Logistics Regression
Section 07: Classification Trees
Section 08: Data Cleaning
Section 09: Data Models
Section 10: Learning Success
Course 02: Hands-on Machine Learning Project - Auto Image Captioning for Social Media
Section 01: Introduction
Section 02: Building the Auto Image Captioning
Section 03: Deployment of Machine Learning App
Assessment Process of Machine Learning & Artificial Intelligence
Once you have completed all the courses in the Machine Learning & Artificial Intelligence bundle, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards.
 CPD 
60 CPD hours / points  Accredited by CPD Quality Standards



 Who is this course for? 
This Machine Learning & Artificial Intelligence bundle is suitable for everyone.




 Requirements 
You will not need any prior background or expertise in this Machine Learning & Artificial Intelligence.




 Career path 
This Machine Learning & Artificial Intelligence bundle will allow you to kickstart or take your career in the related sector to the next stage.



 Certificates 
CPD Accredited Digital certificate
 Digital certificate - Included 
CPD Accredited Hard copy certificate
 Hard copy certificate - £29 
If you are an international student, you will be required to pay an additional fee of 10 GBP for international delivery, and 4.99 GBP for delivery within the UK, for each certificate

Learning & Development

Managed Learning Programme (MLP)

Machine Learning (ML)

Trainers Course

ICDL (International Certification of Digital Literacy)

Machine Learning & Artificial Intelligence

<div id="course-pills-1">Duration<p>4 Days</p><p>24 CPD hours</p>This course is intended for<p></p><p>Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure.</p><p></p><p></p><p>AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage?Azure AI Services,?Azure AI Search, and?Azure OpenAI. The course will use C# or Python as the programming language.</p><p></p>Prerequisites<p>Before attending this course, students must have:  </p><li>Knowledge of Microsoft Azure and ability to navigate the Azure portal  </li><li>Knowledge of either C# or Python  </li><li>Familiarity with JSON and REST programming semantics  <br />  Recommended course prerequisites  </li><li>AI-900T00: Microsoft Azure AI Fundamentals course<p></p></li></div><div id="course-pills-2"><h3>1 - Prepare to develop AI solutions on Azure</h3> <ul> <li>Define artificial intelligence</li> <li>Understand AI-related terms</li> <li>Understand considerations for AI Engineers</li> <li>Understand considerations for responsible AI</li> <li>Understand capabilities of Azure Machine Learning</li> <li>Understand capabilities of Azure AI Services</li> <li>Understand capabilities of the Azure Bot Service</li> <li>Understand capabilities of Azure Cognitive Search</li> </ul> <h3>2 - Create and consume Azure AI services</h3> <ul> <li>Provision an Azure AI services resource</li> <li>Identify endpoints and keys</li> <li>Use a REST API</li> <li>Use an SDK</li> </ul> <h3>3 - Secure Azure AI services</h3> <ul> <li>Consider authentication</li> <li>Implement network security</li> </ul> <h3>4 - Monitor Azure AI services</h3> <ul> <li>Monitor cost</li> <li>Create alerts</li> <li>View metrics</li> <li>Manage diagnostic logging</li> </ul> <h3>5 - Deploy Azure AI services in containers</h3> <ul> <li>Understand containers</li> <li>Use Azure AI services containers</li> </ul> <h3>6 - Analyze images</h3> <ul> <li>Provision an Azure AI Vision resource</li> <li>Analyze an image</li> <li>Generate a smart-cropped thumbnail</li> </ul> <h3>7 - Classify images</h3> <ul> <li>Provision Azure resources for Azure AI Custom Vision</li> <li>Understand image classification</li> <li>Train an image classifier</li> </ul> <h3>8 - Detect, analyze, and recognize faces</h3> <ul> <li>Identify options for face detection analysis and identification</li> <li>Understand considerations for face analysis</li> <li>Detect faces with the Azure AI Vision service</li> <li>Understand capabilities of the face service</li> <li>Compare and match detected faces</li> <li>Implement facial recognition</li> </ul> <h3>9 - Read Text in images and documents with the Azure AI Vision Service</h3> <ul> <li>Explore Azure AI Vision options for reading text</li> <li>Use the Read API</li> </ul> <h3>10 - Analyze video</h3> <ul> <li>Understand Azure Video Indexer capabilities</li> <li>Extract custom insights</li> <li>Use Video Analyzer widgets and APIs</li> </ul> <h3>11 - Analyze text with Azure AI Language</h3> <ul> <li>Provision an Azure AI Language resource</li> <li>Detect language</li> <li>Extract key phrases</li> <li>Analyze sentiment</li> <li>Extract entities</li> <li>Extract linked entities</li> </ul> <h3>12 - Build a question answering solution</h3> <ul> <li>Understand question answering</li> <li>Compare question answering to Azure AI Language understanding</li> <li>Create a knowledge base</li> <li>Implement multi-turn conversation</li> <li>Test and publish a knowledge base</li> <li>Use a knowledge base</li> <li>Improve question answering performance</li> </ul> <h3>13 - Build a conversational language understanding model</h3> <ul> <li>Understand prebuilt capabilities of the Azure AI Language service</li> <li>Understand resources for building a conversational language understanding model</li> <li>Define intents, utterances, and entities</li> <li>Use patterns to differentiate similar utterances</li> <li>Use pre-built entity components</li> <li>Train, test, publish, and review a conversational language understanding model</li> </ul> <h3>14 - Create a custom text classification solution</h3> <ul> <li>Understand types of classification projects</li> <li>Understand how to build text classification projects</li> </ul> <h3>15 - Create a custom named entity extraction solution</h3> <ul> <li>Understand custom named entity recognition</li> <li>Label your data</li> <li>Train and evaluate your model</li> </ul> <h3>16 - Translate text with Azure AI Translator service</h3> <ul> <li>Provision an Azure AI Translator resource</li> <li>Specify translation options</li> <li>Define custom translations</li> </ul> <h3>17 - Create speech-enabled apps with Azure AI services</h3> <ul> <li>Provision an Azure resource for speech</li> <li>Use the Azure AI Speech to Text API</li> <li>Use the text to speech API</li> <li>Configure audio format and voices</li> <li>Use Speech Synthesis Markup Language</li> </ul> <h3>18 - Translate speech with the Azure AI Speech service</h3> <ul> <li>Provision an Azure resource for speech translation</li> <li>Translate speech to text</li> <li>Synthesize translations</li> </ul> <h3>19 - Create an Azure AI Search solution</h3> <ul> <li>Manage capacity</li> <li>Understand search components</li> <li>Understand the indexing process</li> <li>Search an index</li> <li>Apply filtering and sorting</li> <li>Enhance the index</li> </ul> <h3>20 - Create a custom skill for Azure AI Search</h3> <ul> <li>Create a custom skill</li> <li>Add a custom skill to a skillset</li> </ul> <h3>21 - Create a knowledge store with Azure AI Search</h3> <ul> <li>Define projections</li> <li>Define a knowledge store</li> </ul> <h3>22 - Plan an Azure AI Document Intelligence solution</h3> <ul> <li>Understand AI Document Intelligence</li> <li>Plan Azure AI Document Intelligence resources</li> <li>Choose a model type</li> </ul> <h3>23 - Use prebuilt Azure AI Document Intelligence models</h3> <ul> <li>Understand prebuilt models</li> <li>Use the General Document, Read, and Layout models</li> <li>Use financial, ID, and tax models</li> </ul> <h3>24 - Extract data from forms with Azure Document Intelligence</h3> <ul> <li>What is Azure Document Intelligence?</li> <li>Get started with Azure Document Intelligence</li> <li>Train custom models</li> <li>Use Azure Document Intelligence models</li> <li>Use the Azure Document Intelligence Studio</li> </ul> <h3>25 - Get started with Azure OpenAI Service</h3> <ul> <li>Access Azure OpenAI Service</li> <li>Use Azure OpenAI Studio</li> <li>Explore types of generative AI models</li> <li>Deploy generative AI models</li> <li>Use prompts to get completions from models</li> <li>Test models in Azure OpenAI Studio's playgrounds</li> </ul> <h3>26 - Build natural language solutions with Azure OpenAI Service</h3> <ul> <li>Integrate Azure OpenAI into your app</li> <li>Use Azure OpenAI REST API</li> <li>Use Azure OpenAI SDK</li> </ul> <h3>27 - Apply prompt engineering with Azure OpenAI Service</h3> <ul> <li>Understand prompt engineering</li> <li>Write more effective prompts</li> <li>Provide context to improve accuracy</li> </ul> <h3>28 - Generate code with Azure OpenAI Service</h3> <ul> <li>Construct code from natural language</li> <li>Complete code and assist the development process</li> <li>Fix bugs and improve your code</li> </ul> <h3>29 - Generate images with Azure OpenAI Service</h3> <ul> <li>What is DALL-E?</li> <li>Explore DALL-E in Azure OpenAI Studio</li> <li>Use the Azure OpenAI REST API to consume DALL-E models</li> </ul> <h3>30 - Use your own data with Azure OpenAI Service</h3> <ul> <li>Understand how to use your own data</li> <li>Add your own data source</li> <li>Chat with your model using your own data</li> </ul> <h3>31 - Fundamentals of Responsible Generative AI</h3> <ul> <li>Plan a responsible generative AI solution</li> <li>Identify potential harms</li> <li>Measure potential harms</li> <li>Mitigate potential harms</li> <li>Operate a responsible generative AI solution</li> </ul></div>

Microsoft AZ-104

Azure Cognitive Services

AI-102T00 Designing and Implementing an Azure AI Solution

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 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>

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