Name: Data Science with Python Course
Price: 35 GBP
Availability: InStock

<strong>In this course, you will learn how to unlock the power of Generative AI and learn to generate passive income for profitable online ventures with ChatGPT. Gain the skills to automate tasks efficiently, generate revenue from your content, and uphold ethical AI practices. This course is perfect for beginners or anyone seeking to enhance their online ventures with AI technology.</strong>
        <p>Welcome! This comprehensive course is designed to help you discover the potential of ChatGPT and AI, and leverage it to create passive income streams.

This course offers a range of valuable skills across multiple sections. In the Writing section, you will discover how to utilize ChatGPT to create various content types, such as eBooks, articles, poetry, and sales copy for websites. Additionally, you will learn how to publish your eBooks on Kindle and earn revenue through Amazon Associates Program.

In the Photos and Digital Art section, you will explore how to leverage Midjourney to generate stock photos and phone backgrounds for Zedge. You will also learn how to dropship merchandise, which can create an additional source of passive income. In the Coding section, you will learn how to use ChatGPT to develop websites and design custom projects for clients on platforms like Upwork and Fiverr.

In the Social Media section, you will gain insight into how to automate ChatGPT using Zapier, which can assist in creating social media connections. In the Services section, you will acquire the skills to produce tailored nutrition and life coaching plans using ChatGPT. You will also learn how to read and evaluate resumes for others. In the Ethics section, you will delve into the significance of ethical AI and responsible use of AI for generating passive income.

By the end of this course, you will have the knowledge and skills to leverage the power of ChatGPT and AI to generate passive income in a variety of ways.</p>
        <h2>What You Will Learn</h2><p>Use ChatGPT to create children's story eBooks, write articles, and more<br>Use ChatGPT to create effective copywriting for ads and emails<br>Create beautiful digital art and photos with Midjourney and Zedge<br>Use ChatGPT to code a website and code for others using Upwork and Fiverr<br>Automate ChatGPT with Zapier and connect it to social media platforms<br>Create customized nutrition plans and life coaching plans</p>
        <h2>Audience</h2><p>This course is designed for individuals who are interested in using AI technology to generate passive income online. It is suitable for anyone who wants to learn how to utilize generative AI to create content for writing, photography, digital art, coding, and social media. Whether you are a freelancer, content creator, entrepreneur, or simply looking to diversify your income streams, this course will provide you with the tools and knowledge to leverage AI technology for profitable online ventures. No prior technical knowledge or coding experience is required, as the course will walk you through each step in a user-friendly and accessible manner.</p>
        <h2>Approach</h2><p>The course follows a practical, hands-on approach with step-by-step guidance, enabling learners to quickly acquire the skills needed to harness the power of Generative AI for their online ventures.</p>
        <h2>Key Features</h2><p>Utilization of ChatGPT AI for various tasks such as writing, coding, and nutrition planning * Emphasis on practical skills, such as creating eBooks and stock photos, and automating social media * Explore various creative mediums such as writing, digital art, and social media</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/How-to-Use-ChatGPT-and-Generative-AI-for-Passive-Income </p>
        <h2>About the Author</h2><h3>Delta X Technology LLC</h3><p>Delta X Technology LLC is owned by Justin B, a highly skilled software engineer and AI creator with 5 years of industry experience. With a strong background in both theoretical and practical aspects of software engineering, Justin has worked on several projects involving machine learning. As the owner of Delta X Technology LLC, he is dedicated to sharing his expertise with students and helping them develop the skills needed to succeed in the field of AI. With a passion for teaching and a wealth of real-world experience, Justin offers students a unique perspective and valuable insights into the latest trends and best practices in AI development.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Writing</h4><p></p><br><h4>3. Photos and Digital Art</h4><p></p><br><h4>4. Coding</h4><p></p><br><h4>5. Social Media</h4><p></p><br><h4>6. Services</h4><p></p><br><h4>7. Ethics</h4><p></p>

Packt

Coding

Generative AI

Ethics

Web Development

Writing

Freelancing

Automation

ChatGPT

In this course, you will learn how to unlock the power of Generative AI and learn to generate passive income for profitable online ventures with ChatGPT. Gain the skills to automate tasks efficiently, generate revenue from your content, and uphold ethical AI practices. This course is perfect for beginners or anyone seeking to enhance their online ventures with AI technology.

How to Use ChatGPT and Generative AI for Passive Income

General Admission

<strong>This course is intended for beginner-level individuals who are fascinated about quantum computing and want to learn more about it. It uses Jupyter notebook and IBM Qiskit tool to execute your learning into the actual computation.</strong>
        <p>Quantum computers could help the development of new breakthroughs in science, medications, machine learning, material science, and finance, which will help mankind become the best civilization in the whole universe. In fact, quantum computing is so powerful that no one knows how to use its true potential and till now, no quantum algorithm is perfect. The hardware and code are still in development stages, providing great opportunities in the future for quantum computing professionals.

This course starts by introducing you to the basics of current classical computing technology that is based on bits/binary digits (0 and 1) and quantum computing (qubits), and how it is way much more advanced than earlier. Then you will install and get tested on working with the Jupyter notebook and IBM Qiskit in order to execute codes. Lastly, you will learn all the quantum computing concepts and their execution simultaneously in a much leaner, simpler, and more concise format.

By the end of this course, you will learn the basics of quantum computing through implementing it via IBM Qiskit, and you could be able to contribute yourself to the domain that is still in the development phase and be a part of great opportunities in the future for quantum computing professionals.

The resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Dummies-Guide-to-Practical-Quantum-Computing-with-IBM-Qiskit</p>
        <h2>What You Will Learn</h2><p>Learn essential details about quantum mechanics<br>Install and test Qiskit, the quantum framework by IBM<br>Code quantum circuit using Pauli x-gate and other gates<br>Learn eigenvalue and eigenvectors<br>Create a multi-qubit circuit using single-qubit gates<br>Learn the Deutsch-Jozsa (DJ) algorithm, which demonstrates quantum parallelism</p>
        <h2>Audience</h2><p>The course is best suited for beginners who want to start with practical quantum computing concepts.<br><br>Basic computer knowledge and enthusiasm about quantum computing are the only prerequisites to excel and extract the most out of this course.</p>
        <h2>Approach</h2><p>This course is divided into smaller modules followed by hands-on examples performed on IBM Qiskit documentation. Almost all of the illustrations and examples used in this course are from the IBM Qiskit documentation website, which may be further referred to at any point of time in the future.</p>
        <h2>Key Features</h2><p>Learn and understand about general quantum computing based on IBM Qiskit documentation * Learn quantum key distribution, which utilizes the unique properties of quantum systems * Learn quantum teleportation used for transferring quantum information from a sender to a receiver</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Dummies-Guide-to-Practical-Quantum-Computing-with-IBM-Qiskit</p>
        <h2>About the Author</h2><h3>Abhilash Nelson</h3><p>Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than 8 years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.</p>
        <h2>Course Outline</h2><h4>1. Course Introduction and Table of Contents</h4><p></p><br><h4>2. Introduction to Quantum Mechanics</h4><p></p><br><h4>3. Classical Bit vs Quantum Qubit</h4><p></p><br><h4>4. Creating, Retaining and Reading out Qubits</h4><p></p><br><h4>5. Vector and Matrix Quantum States</h4><p></p><br><h4>6. Classic Logic Gates Overview</h4><p></p><br><h4>7. Popular Quantum Frameworks</h4><p></p><br><h4>8. Installing Anaconda Python Distribution</h4><p></p><br><h4>9. Installing and Testing Qiskit</h4><p></p><br><h4>10. Pauli X-gate in Qiskit</h4><p></p><br><h4>11. Pauli X-gate input and output customizations</h4><p></p><br><h4>12. Pauli X-gate in Real IBM Quantum Computer</h4><p></p><br><h4>13. Pauli Matrixes as State Vectors</h4><p></p><br><h4>14. Pauli Y-gate Operations</h4><p></p><br><h4>15. Pauli Z-gate</h4><p></p><br><h4>16. Eigen Vectors of XYZ gates</h4><p></p><br><h4>17. Hadamard Gate Introduction</h4><p></p><br><h4>18. Hadamard Gate in Qiskit</h4><p></p><br><h4>19. Hadamard Gate Exercises</h4><p></p><br><h4>20. H gate in Real Quantum Computer</h4><p></p><br><h4>21. R phi Gate</h4><p></p><br><h4>22. S and T Gates</h4><p></p><br><h4>23. U and I Gates</h4><p></p><br><h4>24. Multi Qubit states introduction</h4><p></p><br><h4>25. Representing Multi Qubit States</h4><p></p><br><h4>26. Multi Qubit Circuit using Single Qubit Gates - sample circuit 1</h4><p></p><br><h4>27. Multi Qubit Circuit using Single Qubit Gates - sample circuit 2</h4><p></p><br><h4>28. CNOT gate with classical Qubits</h4><p></p><br><h4>29. CNOT gate with control qubit superposition</h4><p></p><br><h4>30. CNOT gate with control qubit superposition - In Real Quantum Computer</h4><p></p><br><h4>31. CNOT gate with both qubit superposition</h4><p></p><br><h4>32. CNOT gate with both qubit superposition target x</h4><p></p><br><h4>33. CNOT Circuit Identities</h4><p></p><br><h4>34. CZ Circuit Identity</h4><p></p><br><h4>35. CY Circuit Identity</h4><p></p><br><h4>36. SWAP Circuit Identity</h4><p></p><br><h4>37. Toffoli Gate</h4><p></p><br><h4>38. Toffoli Circuit Identity</h4><p></p><br><h4>39. DJ Problem Overview</h4><p></p><br><h4>40. DJ Algorithm Design</h4><p></p><br><h4>41. DJ Algorithm Implementation</h4><p></p><br><h4>42. Quantum Cryptography : Quantum Key Distribution</h4><p></p><br><h4>43. Quantum Teleportation Theory</h4><p></p><br><h4>44. Further Learning and Resources</h4><p></p>

Qiskit

Quantum Computing

Programming

This course is intended for beginner-level individuals who are fascinated about quantum computing and want to learn more about it. It uses Jupyter notebook and IBM Qiskit tool to execute your learning into the actual computation.

Beginners' Guide to Practical Quantum Computing with IBM Qiskit

<p><strong>Python programmers</strong> are in great demand for a variety of reasons, including the language's widespread use and popularity as well as the fact that Python has emerged as a viable alternative in several fields. Developing web applications, working with data science, and machine learning can be done through Python codes. As a result, expertise in Python can open up a wide range of opportunities for you. If you want to develop your skills from scratch, or just want to refresh your old skills, we got you covered. We offer a <strong>Python Programming at QLS Level 7</strong> course discussing the basics to advanced techniques.</p><p><strong> Enrol this course and achieve the Quality Licence Scheme endorsed certificate </strong></p><p>Learners who are highly motivated and computer literate and who want to learn how to program in python should enrol in this course. It is great for developing your coding skills. We focused on a vast array of tools and technologies in this extensive course, including:</p><ul><li>Introduction to Python Programming from A-Z</li><li>Getting Familiar with Python</li><li>Basic Data Types</li><li>Python Operators</li><li>Advanced-Data Types</li><li>Control Flow Part 1</li><li>Control Flow Part 2</li><li>Functions</li><li>User Input and Error Handling</li><li>Python Advanced Functions</li><li>Python Scripting and Libraries</li><li>NumPy</li><li>Pandas</li><li>Introduction to OOP</li><li>Advanced OOP</li></ul><p>In the UK, there are more than <strong>750 new job posts for software engineers</strong> every day, and Python is the second-most in-demand programming language among companies. So, if you want to manage your future and make money by learning new skills, Python can be your best bet.</p><p>This course training includes 1 of the best-selling course endorsed by the Quality Licence Scheme with an endorsed certificate of achievement, expert assistance, and course assessment. Enjoy 12 months of free access to this course and master a range of in-demand professional skills to advance your career. <strong>No hidden costs - simply enrol this course.</strong></p><p><strong>Our course is endorsed by the Quality Licence Scheme, ensuring your newly acquired skills will enhance your professional development. This course is accredited by CPD.</strong></p><p>Dedicated learning assistance and 24/7 customer support are available to all students with this premium quality this course. Get the help you require and the answers to all your queries throughout the comprehensive syllabus of this course.</p><p>With this Python Programming at QLS Level 7 training, success becomes much easier and enables you to monetise your skills. This course is designed for both part-time and full-time students and can be completed at a pace that suits your learning style.</p><p><strong>Curriculum Breakdown:</strong></p><ul><li><strong>Module 01: Introduction to Python Programming from A-Z<br /></strong></li><li><strong>Module 02: Getting Familiar with Python<br /></strong></li><li><strong>Module 03: Basic Data Types<br /></strong></li><li><strong>Module 04: Python Operators<br /></strong></li><li><strong>Module 05: Advanced Data Types<br /></strong></li><li><strong>Module 06: Control Flow Part 1<br /></strong></li><li><strong>Module 07: Control Flow Part 2<br /></strong></li><li><strong>Module 08: Python Functions<br /></strong></li><li><strong>Module 09: User Input and Error Handling<br /></strong></li><li><strong>Module 10: Python Advanced Functions<br /></strong></li><li><strong>Module 11: Python Scripting and Libraries<br /></strong></li><li><strong>Module 12: NumPy<br /></strong></li><li><strong>Module 13: Pandas<br /></strong></li><li><strong>Module 14: Introduction to OOP<br /></strong></li><li><strong>Module 15: Advanced OOP<br /></strong></li><li><strong>Module 16: Starting a Career in Python</strong></li></ul><p><strong>Key Highlights</strong></p><ul><li>The Quality Licence Scheme ensures that the course you enrolled in, Python Programming at QLS Level 7, is of the highest quality.</li><li>It helps you to get recognised by employers easily.</li><li>The endorsed certificate of this course will make you stand out from the crowd when applying for jobs during this pandemic.</li><li>The Certificate of Achievement Endorsed by the Quality Licence Scheme has helped thousands of people reach their professional goals and get better jobs.</li></ul><p><strong>Why People Enrol from One Education:</strong></p><ul><li>Receive a Quality Licence Scheme-endorsed certificate upon successful completion of the course</li><li>Learning materials of the course contain engaging voiceover and visual elements for your comfort</li><li>Get 24/7 access to all course content for a full year</li><li>Each of our students gets full tutor support on weekdays (Monday to Friday)</li><li>Efficient assessments and instant results upon completion of the course</li></ul><p>This uniquely tailored course makes online learning as efficient and easy as possible. This this course will motivate you to learn, develop, and participate in an exceptional e-learning journey while upgrading your CV &amp; competence and introducing you to new possibilities to further your career development.</p><p><strong>Course Assessment</strong></p><p>You will immediately be given access to a specifically crafted MCQ test upon completing an online module. For each test, the pass mark will be set to 60%.</p><p><strong>Certificate of Achievement</strong></p><p><strong>QLS Endorsed Certificate </strong></p><p>After completing this course, learners will get an endorsed certificate (Completly FREE) delivered right at their doorstep by post, without paying a single penny! Please note that learners outside the UK must pay a £10 postage charge.</p><p><strong>Accredited Certificate </strong></p><p>After completing the MCQ assessment of this course, you will also qualify for a CPD QS Accredited Certificate from One Education as proof of your continued professional development. Certification is available in PDF format at the cost of £9, or a hard copy can be sent to you via post at the expense of £15.</p><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This course is ideal for highly motivated individuals or teams who want to enhance their professional skills and efficiently skilled employees.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal entry requirements for this course, with enrollment open to anyone!</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This course can help you to develop your career by helping you to apply for high-paying employment in the related fields.<br />Enrol now and take your career to the next step.</p>
                        </div>

One Education

Python Programming at QLS Level 7 Course

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.</p>Overview<p>By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data.</p><p>This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive.<br /><br />You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions.</p></div><div id="course-pills-2"><h4>Data Exploration and Cleaning</h4><ul><li>Python and the Anaconda Package Management System</li><li>Different Types of Data Science Problems</li><li>Loading the Case Study Data with Jupyter and pandas</li><li>Data Quality Assurance and Exploration</li><li>Exploring the Financial History Features in the Dataset</li><li>Activity 1: Exploring Remaining Financial Features in the Dataset</li></ul><h4>Introduction to Scikit-Learn and Model Evaluation</h4><ul><li>Introduction</li><li>Model Performance Metrics for Binary Classification</li><li>Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve</li></ul><h4>Details of Logistic Regression and Feature Exploration</h4><ul><li>Introduction</li><li>Examining the Relationships between Features and the Response</li><li>Univariate Feature Selection: What It Does and Doesn't Do</li><li>Building Cloud-Native Applications</li><li>Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients</li></ul><h4>The Bias-Variance Trade-off</h4><ul><li>Introduction</li><li>Estimating the Coefficients and Intercepts of Logistic Regression</li><li>Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters</li><li>Activity 4: Cross-Validation and Feature Engineering with the Case Study Data</li></ul><h4>Decision Trees and Random Forests</h4><ul><li>Introduction</li><li>Decision trees</li><li>Random Forests: Ensembles of Decision Trees</li><li>Activity 5: Cross-Validation Grid Search with Random Forest</li></ul><h4>Imputation of Missing Data, Financial Analysis, and Delivery to Client</h4><ul><li>Introduction</li><li>Review of Modeling Results</li><li>Dealing with Missing Data: Imputation Strategies</li><li>Activity 6: Deriving Financial Insights</li><li>Final Thoughts on Delivering the Predictive Model to the Client</li></ul></div>

Nexus Human

Predictive Analytics

Data Engineering

Big Data Analytics

Data Science

Data Mining

Data Skills

Data Science Projects with Python

<p><strong> Sale Ends Today <br /><br /> JavaScript Application Programming - CPD Certified Admission Gifts  <br /><br /> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter</strong></p><p>Till date, <strong>JavaScript</strong> remains the <strong>backbone of interactive web applications </strong>worldwide, with <strong>over 95% </strong>of all websites using it in some form. In the rapidly evolving digital landscape, mastering JavaScript is more than a skill, rather, it's a necessity. The <strong>JavaScript Application Programming</strong> bundle is designed to transform you from a novice to a master developer, and elevate your programming skills to set the web on fire.</p><p>The <strong>JavaScript Application Programming</strong> bundle provides a comprehensive exploration into JavaScript and its powerful ecosystem. Starting with <strong>JavaScript Foundations for Everyone</strong>, the course progressively covers advanced topics such as <strong>JavaScript Functions</strong> and <strong>JavaScript Promises</strong>, ensuring a deep understanding of core concepts. Additional modules like <strong>jQuery</strong>, <strong>WebGL 3D Programming</strong>, and <strong>Web GIS Application Development</strong> with <strong>ASP.NET CORE MVC</strong> expand your skillset into creating dynamic, data-driven web applications along with courses in other essential languages such as <strong>Python</strong>, <strong>C#</strong>, and <strong>SQL</strong>.</p><p><strong>Courses Are Included In This JavaScript Application Programming - CPD Certified Bundle: </strong></p><ul><li><strong> Course 01: </strong>JavaScript Foundations for Everyone</li><li><strong> Course 02: </strong>JavaScript Functions</li><li><strong> Course 03: </strong>JavaScript Promises</li><li><strong> Course 04: </strong>jQuery: JavaScript and AJAX Coding Bible</li><li><strong> Course 05: </strong>Java Certification Cryptography Architecture</li><li><strong> Course 06: </strong>Master JavaScript with Data Visualisation</li><li><strong> Course 07: </strong>Coding Essentials - Javascript, ASP. Net, C# - Bonus HTML</li><li><strong> Course 08: </strong>Kotlin Programming: Android Coding Bible</li><li><strong> Course 09: </strong>Secure Programming of Web Applications</li><li><strong> Course 10: </strong>Web GIS Application Development with C# ASP.NET CORE MVC and Leaflet</li><li><strong> Course 11: </strong>Mastering SQL Programming</li><li><strong> Course 12: </strong>Complete Microsoft SQL Server from Scratch: Bootcamp</li><li><strong> Course 13: </strong>Ultimate PHP &amp; MySQL Web Development &amp; OOP Coding</li><li><strong> Course 14: </strong>SQL for Data Science, Data Analytics and Data Visualisation</li><li><strong> Course 15: </strong>Quick Data Science Approach from Scratch</li><li><strong> Course 16: </strong>Python Programming Bible</li><li><strong> Course 17: </strong>Python Programming from Scratch with My SQL Database</li><li><strong> Course 18: </strong>Machine Learning with Python Course</li><li><strong> Course 19: </strong>HTML Web Development Crash Course</li><li><strong> Course 20: </strong>CSS Web Development</li><li><strong> Course 21: </strong>Three.js &amp; WebGL 3D Programming</li><li><strong> Course 22: </strong>Basics of WordPress: Create Unlimited Websites</li><li><strong> Course 23: </strong>Masterclass Bootstrap 5 Course - Responsive Web Design</li><li><strong> Course 24: </strong>C++ Development: The Complete Coding Guide</li><li><strong> Course 25: </strong>C# Basics</li><li><strong> Course 26: </strong>C# Programming - Beginner to Advanced</li><li><strong> Course 27: </strong>Stripe with C#</li><li><strong> Course 28: </strong>C# Console and Windows Forms Development with LINQ &amp; ADO.NET</li><li><strong> Course 29: </strong>Cyber Security Incident Handling and Incident Response</li><li><strong> Course 30: </strong>Computer Networks Security from Scratch to Advanced</li></ul><p>Don't miss the chance to become a versatile and highly skilled software developer with our <strong>JavaScript Application Programming</strong> course. 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Enrol today to take the first step towards a lucrative career in software development, where your ability to innovate and solve complex problems will shape the future of technology!</p><p><strong>Learning Outcomes of this Bundle:</strong></p><ul><li>Master fundamental and advanced JavaScript programming techniques.</li><li>Learn to implement interactive web features using jQuery and AJAX.</li><li>Develop proficiency in data visualisation with JavaScript.</li><li>Understand and apply Python, C#, and SQL in web development contexts.</li><li>Create responsive and secure web applications using Bootstrap and C#.</li><li>Manage and deploy sophisticated web-based GIS applications.</li></ul><p><strong>With this JavaScript Application Programming - CPD Certified course, you will get 30 CPD Accredited PDF Certificates, a Hard Copy Certificate and our exclusive student ID card absolutely free.</strong></p><p><strong>Why Prefer this Course?</strong></p><ul><li>Get a <strong>Free CPD Accredited Certificate</strong> upon completion of the course</li><li>Get a <strong>Free Student ID Card</strong> with this training program (£10 postal charge will be applicable for international delivery)</li><li>The course is <strong>Affordable and Simple</strong> to understand</li><li>Get <strong>Lifetime Access</strong> to the course materials</li><li>The training program comes with <strong>24/7 Tutor Support</strong></li></ul> <p><strong>Start your learning journey straight away!</strong></p><p>The <strong>JavaScript Application Programming</strong> course bundle is an exceptional resource for those looking to dive deep into the world of modern web development. By mastering fundamental and advanced <strong>JavaScript programming techniques</strong>, learners will gain the ability to create dynamic and interactive web applications that are both efficient and visually appealing. This foundational knowledge is essential, as JavaScript remains a critical tool for front-end development, enabling developers to implement complex features that improve user experience and site functionality.</p><p>Beyond the basics, the course introduces students to <strong>data visualisation with JavaScript</strong>, which is an increasingly important skill in the tech industry. Additionally, the inclusion of frameworks like <strong>jQuery</strong> and <strong>Bootstrap</strong> ensures that learners can streamline their coding process and design responsive layouts that adapt to different devices, a must-have in today's mobile-first world. Learners will also develop proficiency in other programming languages such as <strong>Python</strong>, <strong>C#</strong>, and <strong>SQL</strong>, which are integral to backend development and database management.</p><p><strong>Moreover, this diploma offers learners the opportunity to acquire a Recognised Qualification that is highly valued in the field of JavaScript. With this Certification, graduates are better positioned to pursue career advancement and higher responsibilities within the JavaScript setting. The skills and knowledge gained from this course will enable learners to make meaningful contributions to JavaScript related fields impacting their JavaScript experiences and long-term development.</strong></p><p><strong> Course Curriculum </strong></p><p><strong> Course 01: JavaScript Foundations for Everyone</strong></p><ul><li>Module 01: About the Author</li><li>Module 02: Introduction to JavaScript</li><li>Module 03: Strengths and Weaknesses of JavaScript</li><li>Module 04: Writing JavaScript in Chrome</li><li>Module 05: JavaScript Variables</li><li>Module 06: Demo of JavaScript Variables</li><li>Module 07: Basic Types of JavaScript</li><li>Module 08: JavaScript Boolean</li><li>Module 09: JavaScript Strings</li><li>Module 10: JavaScript Numbers</li><li>Module 11: JavaScript Objects</li><li>Module 12: Demo of JavaScript Objects</li><li>Module 13: JavaScript Arrays</li><li>Module 14: Demo of JavaScript Arrays</li><li>Module 15: JavaScript Functions</li><li>Module 16: Demo of JavaScript Functions</li><li>Module 17: JavaScript Scope and Hoisting</li><li>Module 18: Demo of JavaScript Scope and Hoisting</li><li>Module 19: Currying Functions</li><li>Module 20: Demo of Currying Functions</li><li>Module 21: Timeouts and Callbacks</li><li>Module 22: Demo of Timeouts and Callbacks</li><li>Module 23: JavaScript Promises</li><li>Module 24: Demo of JavaScript Promises</li><li>Module 25: Demo of Javascript Async</li><li>Module 26: Flow Control</li><li>Module 27: Demo of Flow Control</li><li>Module 28: JavaScript For Loop</li><li>Module 29: Demo of JavaScript For Loop</li><li>Module 30: Demo of Switch Statements</li><li>Module 31: Error Handling</li><li>Module 32: Demo Project</li></ul><p><strong> Course 02: JavaScript Functions</strong></p><ul><li>Module 01: Introduction</li><li>Module 02: Defining And Invoking Functions</li><li>Module 03: Function Scope</li><li>Module 04: Composing Functions</li><li>Module 05: Asynchronous Functions</li></ul><p><strong> Course 03: JavaScript Promises</strong></p><ul><li>Module 01: JavaScript Promises</li><li>Module 02: Understanding Promises</li><li>Module 03: Using Promises</li><li>Module 04: Multiple Promises</li><li>Module 05: Handling Errors With Promises</li></ul><p><strong>=========&gt;&gt;&gt;&gt;&gt; And 27 More Courses &lt;&lt;&lt;&lt;&lt;=========</strong></p><p><strong>How will I get my Certificate?</strong></p><p>After successfully completing the course, you will be able to order your Certificates as proof of your achievement.</p><ul><li><strong>PDF Certificate:</strong> Free (Previously it was £12.99*30 = £390)</li><li><strong>CPD Hard Copy Certificate: </strong>Free (For The First Course: Previously it was £29.99)</li></ul> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>Anyone interested in learning more about the topic is advised to take this bundle. This bundle is ideal for:</p><ul><li>Aspiring software developers.</li><li>Web developers.</li><li>Computer science students.</li><li>Tech industry professionals.</li><li>Entrepreneurs in tech.</li><li>Career changers.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

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

                        <div id="career_path">
                            <p>After completing this bundle, you are to start your career or begin the next phase of your career.</p><ul><li>Web Developer: $40,000 - $85,000</li><li>Software Developer: $50,000 - $120,000</li><li>Full-Stack Developer: $60,000 - $120,000</li><li>Data Analyst: $45,000 - $85,000</li><li>Systems Engineer: $60,000 - $130,000</li><li>Cyber Security Analyst: $60,000 - $100,000</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited  Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><ul><li><strong>CPD Accredited e-Certificate - Free</strong></li><li><strong>Enrolment Letter - Free</strong></li><li><strong>Student ID Card - Free</strong></li></ul></div>
        <h4>CPD Accredited Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p><strong><em>Please note that International students have to pay an additional £10 as a shipment fee.</em></strong></p></div>

                        </div>

Academy for Health and Fitness

Online Course Marketing

Online Course Creation

Salesforce Certified JavaScript Developer

AWS Certified Developer

Computer Programming

Professional Skills Course (PSC)

JavaScript Application Programming - CPD Certified

<p><strong> Sale Ends Today </strong></p><p><strong> IT Skills for Business Level 3</strong><strong> Admission Gifts </strong></p><p><strong> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter </strong></p><p>IT skills are not just beneficial today, rather they have become indispensable for any business environment. With <strong>over 1.46 million people</strong> working within the <strong>digi-tech sector</strong> in the UK, the demand for advanced IT competencies continues to surge. The <strong>IT Skills for Business Level 3</strong> course bundle is designed to place you at the forefront of this dynamic field. So, get ready to elevate your professional capability and become an invaluable asset to any business!</p><p>The <strong>IT Skills for Business Level 3</strong> course offers an extensive curriculum tailored to enhance your technical skills across a broad spectrum of IT disciplines. From foundational courses like <strong>IT Support Technician</strong> and <strong>Functional Skills IT</strong> to specialised training in <strong>Ethical Hacking</strong>, <strong>Cyber Security</strong>, and <strong>Network Security</strong>, this bundle equips you with the necessary tools to secure, analyse, and manage IT infrastructure effectively. Additionally, enhance your strategic acumen with <strong>Business Analysis</strong>, learning to translate business needs into IT solutions, and exploring <strong>Financial Analysis</strong> for richer business decision-making.</p><p><strong>Courses Are Included In this IT Skills for Business Level 3 Career Bundle:</strong></p><ul><li><strong> Course 01: </strong>IT Support Technician</li><li><strong> Course 02: </strong>Functional Skills IT</li><li><strong> Course 03: </strong>Building Your Own Computer Course</li><li><strong> Course 04: </strong>It: Ethical Hacking, IT Security and IT Cyber Attacking</li><li><strong> Course 05: </strong>Computer Operating System and Troubleshooting</li><li><strong> Course 06: </strong>Cyber Security Incident Handling and Incident Response</li><li><strong> Course 07:</strong> Cyber Security Law</li><li><strong> Course 08: </strong>Network Security and Risk Management</li><li><strong> Course 09: </strong>CompTIA Network</li><li><strong> Course 10: </strong>CompTIA Cloud+ (CV0-002)</li><li><strong> Course 11: </strong>Web Application Penetration Testing Course</li><li><strong> Course 12: </strong>Learn Ethical Hacking From A-Z: Beginner To Expert</li><li><strong> Course 13: </strong>C# (C-Sharp)</li><li><strong> Course 14: </strong>JavaScript Fundamentals</li><li><strong> Course 15: </strong>Python Programming Bible</li><li><strong> Course 16: </strong>Data Protection (GDPR) Practitioner</li><li><strong> Course 17: </strong>Microsoft SQL Server Development for Everyone!</li><li><strong> Course 18: </strong>SQL Database Administrator</li><li><strong> Course 19: </strong>Data Science and Visualisation with Machine Learning</li><li><strong> Course 20: </strong>SQL For Data Analytics &amp; Database Development</li><li><strong> Course 21: </strong>Introduction to Business Analysis</li><li><strong> Course 22: </strong>Business Data Analysis</li><li><strong> Course 23: </strong>Financial Analysis for Finance Reports</li><li><strong> Course 24: </strong>Financial Modelling Using Excel</li><li><strong> Course 25: </strong>Data analytics with Excel</li><li><strong> Course 26: </strong>Excel Data Tools and Data Management</li><li><strong> Course 27: </strong>Ultimate Microsoft Excel For Business Bootcamp</li><li><strong> Course 28: </strong>MS Word Essentials - The Complete Word Course - Level 3</li><li><strong> Course 29: </strong>Document Control</li><li><strong> Course 30: </strong>Information Management</li></ul><p>Don't miss the opportunity to transform your career with cutting-edge IT skills that are crucial in today's tech-driven world. Utilise the industry relevant and essential IT knowledge this <strong>IT Skills for Business Level 3</strong> course bundle has to offer and use it as your gateway to success. Enrol today and take the first step towards securing a prominent role in the booming tech industry!</p><p><strong>Learning Outcomes of this Bundle:</strong></p><ul><li>Master key IT support skills and build your own computer.</li><li>Gain proficiency in programming with C#, JavaScript, and Python.</li><li>Develop expertise in cyber security, network security, and risk management.</li><li>Learn comprehensive data protection practices including GDPR compliance.</li><li>Enhance skills in SQL for effective database management and business analytics.</li><li>Apply advanced Excel techniques for financial modelling and data analysis.</li></ul><p><strong>But that's not all. When you enrol in IT Skills for Business Level 3 Bundle, you'll receive 30 CPD-Accredited PDF Certificates, Hard Copy Certificates, and our exclusive student ID card, all absolutely free.</strong></p><p><strong>Why Prefer this Course?</strong></p><ul><li>Get a <strong>Free CPD Accredited Certificate</strong> upon completion of the course</li><li>Get a <strong>Free Student ID Card</strong> with this training program (£10 postal charge will be applicable for international delivery)</li><li>The course is <strong>Affordable and Simple</strong> to understand</li><li>Get <strong>Lifetime Access</strong> to the course materials</li><li>The training program comes with <strong>24/7 Tutor Support</strong></li></ul> <p><strong>Start your learning journey straight away!</strong></p><p>The <strong>"IT Skills for Business Level 3"</strong> course bundle is an invaluable resource for anyone looking to deepen their understanding and expertise in the diverse fields of <strong>IT</strong> and <strong>Business Analysis</strong>. This course offers learners the chance to master foundational <strong>IT Support Skills</strong>, such as <strong>Building their Own Computers </strong>and <strong>troubleshooting </strong>various software issues, providing a solid base from which to expand their knowledge into more specialised areas.</p><p>Further advancement is facilitated through detailed modules focusing on <strong>Business Analysis Skills</strong>, teaching learners how to translate complex business needs into scalable IT solutions. This integration of IT proficiency with <strong>Business </strong>knowledge ensures that participants are well-prepared to tackle strategic challenges, making them invaluable assets to any organisation. By blending IT skills with an understanding of business processes and <strong>Data Analysis</strong>, this course sets up its participants for success in multiple pathways, from <strong>Network Management </strong>to <strong>Business Consulting</strong>.</p><p>Moreover, this diploma offers learners the opportunity to acquire a <strong>Recognised Qualification </strong>that is highly valued in the field of <strong>IT / Business</strong>. With this <strong>Certification</strong>, graduates are better positioned to pursue career advancement and higher responsibilities within the <strong>IT / Business</strong> setting. The skills and knowledge gained from this course will enable learners to make meaningful contributions to <strong>IT / Business</strong> related fields impacting their <strong>IT / Business</strong> experiences and long-term development.</p><p><strong> Course Curriculum </strong></p><p><strong> Course 01: IT Support Technician</strong></p><ul><li>Module 01: Software</li><li>Module 02: Hardware</li><li>Module 03: Security</li><li>Module 04: Networking</li><li>Module 05: Basic IT Literacy</li></ul><p><strong> Course 02: Functional Skills IT</strong></p><ul><li>Module 01: How People Use Computers</li><li>Module 02: System Hardware</li><li>Module 03: Device Ports And Peripherals</li><li>Module 04: Data Storage And Sharing</li><li>Module 05: Understanding Operating Systems</li><li>Module 06: Setting Up And Configuring A PC</li><li>Module 07: Setting Up And Configuring A Mobile Device</li><li>Module 08: Managing Files</li><li>Module 09: Using And Managing Application Software</li><li>Module 10: Configuring Network And Internet Connectivity</li><li>Module 11: IT Security Threat Mitigation</li><li>Module 12: Computer Maintenance And Management</li><li>Module 13: IT Troubleshooting</li><li>Module 14: Understanding Databases</li><li>Module 15: Developing And Implementing Software</li></ul><p><strong> Course 03: Building Your Own Computer Course</strong></p><ul><li>Module 01: Introduction to Computer &amp; Building PC</li><li>Module 02: Overview of Hardware and Parts</li><li>Module 03: Building the Computer</li><li>Module 04: Input and Output Devices</li><li>Module 05: Software Installation</li><li>Module 06: Computer Networking</li><li>Module 07: Building a Gaming PC</li><li>Module 08: Maintenance of Computers</li></ul><p><strong>=========&gt;&gt;&gt;&gt;&gt; And 27 More Courses &lt;&lt;&lt;&lt;&lt;=========</strong></p><p><strong>How will I get my Certificate?</strong></p><p>After successfully completing the course, you will be able to order your Certificates as proof of your achievement.</p><ul><li><strong>PDF Certificate:</strong> Free (Previously it was £12.99*30 = £390)</li><li><strong>CPD Hard Copy Certificate: </strong>Free (For The First Course: Previously it was £29.99)</li></ul> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>Anyone interested in learning more about the topic is advised to take this bundle. This bundle is ideal for:</p><ul><li>Aspiring IT professionals.</li><li>Business analysts.</li><li>Data scientists.</li><li>System administrators.</li><li>Network security specialists.</li><li>Database managers.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

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

                        <div id="career_path">
                            <p>After completing this bundle, you are to start your career or begin the next phase of your career.</p><ul><li>IT Support Specialist: $35,000 - $60,000</li><li>Cyber Security Analyst: $60,000 - $100,000</li><li>Network Engineer: $50,000 - $90,000</li><li>Data Analyst: $45,000 - $85,000</li><li>Software Developer: $50,000 - $120,000</li><li>Database Administrator: $60,000 - $110,000</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><ul><li><strong>CPD Accredited e-Certificate - Free</strong></li><li><strong>Enrolment Letter - Free</strong></li><li><strong>Student ID Card - Free</strong></li></ul></div>
        <h4>CPD Accredited Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>If you are an <strong>international student</strong>, then you have to pay an additional <strong>10 GBP</strong> for each certificate as an international delivery charge.</p></div>

                        </div>

Computing Skills

IT Certifications

IT Skills

Computer Skills

Digital Skills

IT Skills for Business Level 3 - CPD Certified

<p>This Data Science bundle training will help you become an expert in a relevant sector. Our world-class course creators will help you with every query throughout your learning journey and assist you in mastering<strong> Data Science with Python</strong>. So, add this course to your basket   harness the coveted wisdom and open the door to your next breakthrough!</p><p>Whether you're an aspirant looking to start your dream career, a professional looking to branch out, or somebody in between   this <strong>Data Science with Python</strong>, <strong>QLS Endorsed</strong> Course will get you sorted. The fundamental understanding in this course will add tremendous value and enable you to establish the meaningful career you've always craved.</p><p>ENROL NOW in our <strong>'Data Science with Python' </strong>today and Start Learning.</p><p><strong>QLS Endorsed Course:</strong></p><ul><li>Course 01: Python Data Science at QLS Level 1</li></ul><p><strong>â </strong><strong>FREE 10 CPD Courses </strong><strong>â</strong></p><ul><li>Course 01: Python Programming: Beginner To Expert</li><li>Course 02: Higher Order Functions in Python</li><li>Course 03: Python for Spatial Analysis in ArcGIS</li><li>Course 04: Easy to Advanced Data Structures</li><li>Course 05: Google Data Studio</li><li>Course 06: Diploma in Data Analytics with Tableau</li><li>Course 07: Business Intelligence and Data Mining</li><li>Course 08: Mastering SQL Programming</li><li>Course 09: Creativity and Problem-Solving Skills</li><li>Course 10: Time Management</li></ul><p><strong>Why Choose Imperial Academy?</strong></p><ul><li>Fully Online Training</li><li>Free QLS Endorsed &amp; CPD Accredited Certificate</li><li>No Time Limits. Study at Your Own Pace</li><li>Expert Instructors</li><li>Audio Visual Training</li><li>Up-to-date Curriculum and Valuable Guidelines</li></ul><p>Launch the dynamic career you've always wanted! Take this high-quality <strong>Data Science with Python </strong>Bundle training to articulate your innovative goals and develop a simple approach to attaining them.</p><p><strong>Course Curriculum:</strong></p><p><strong>Python Data Science at QLS Level 1</strong></p><ul><li>Module 01: Introduction To Python Data Science</li><li>Module 02: Environment Setup</li><li>Module 03: Numpy Package For Calculations</li><li>Module 04: Panda Package For Data Cleaning</li><li>Module 05: Matplotlib Data Visualization Part 1</li><li>Module 06: Matplotlib Data Visualization Part 2</li></ul><p><strong>------ 10 More FREE CPD Courses ------</strong></p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>This <strong>Data Science with Python </strong>course will be a great source of knowledge to improve your existing aptitudes and take them to the next level.</p><p><strong>Disclaimer</strong></p><p>This <strong>Data Science with Python </strong>course has been endorsed by the <strong>Quality Licence Scheme</strong> for its high-quality, non-regulated provision and training programmes. This <strong>Data Science with Python</strong> course is not regulated by Ofqual and is not an accredited qualification. One Education will be able to advise you on any further recognition. For details, please visit the Learner FAQs on the Quality Licence Scheme website.</p><p>Imperial Academy is an approved reselling partner for <strong>Quality Licence Scheme</strong> courses under One Education.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Anyone can enrol in this <strong>Data Science with Python</strong> bundle course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Scientist: £30,000 - £90,000 per year</li><li>Data Analyst: £20,000 - £50,000 per year</li><li>Business Analyst: £25,000 - £70,000 per year</li><li>Data Engineer: £35,000 - £85,000 per year</li><li>Machine Learning Engineer: £40,000 - £100,000 per year</li><li>Business Intelligence Analyst: £30,000 - £70,000 per year</li><li>Chief Data Officer: £80,000 - £200,000 per year</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p><strong>CPD Accredited Certificate</strong></p><ul><li>PDF Certificate completely free of charge</li><li>Hard Copy for £9.99 each</li><li>International students must pay a £10 shipping cost</li></ul></div>
        <h4>Hard Copy QLS Certificate</h4>
        <p>
            Hard copy certificate -
            £109
        </p>
            <div><p><strong>Quality Licence Scheme (QLS) Endorsed Certificate</strong></p><ul><li>Level 1 - £109</li><li>International students must pay a £10 shipping cost</li></ul></div>

                        </div>

Imperial Academy

QLS Level 1 Course | 10 FREE CPD Courses & PDF Certificates | CPD Accredited | Installment Payment | Tutor Support

Data Science with Python - QLS Endorsed

<p><strong>Learn Python and see the world of programming open up to you!</strong></p><p>Are you ready to take your Python programming skills to the next level? Our comprehensive Python Programming for Data Science Ultimate Bootcamp course covers all the essential topics you need to <strong>become a proficient Python programmer.</strong></p><p>With our course, you'll learn everything from Python programming basics to more advanced topics like <strong>conditional branching, file handling, data storage structures, and exception handling.</strong> You'll also discover how to import external and internal libraries, use string operations, handle date and time, and write user functions in Python.</p><p>This Python Programming for Data Science Ultimate Bootcamp course is perfect for anyone who wants to learn <strong>Python programming for personal or professional reasons</strong>. Whether you're a complete beginner or an experienced programmer, our expert instructors will guide you through the material step-by-step, providing practical exercises and real-world examples.</p><p>Enrol to our Python Programming for Data Science Ultimate Bootcamp course today and discover how to send emails, run programs from the command prompt or Jupyter Notebook, install packages, schedule tasks, and work with databases using SQLite. With our comprehensive curriculum, hands-on learning experience, and expert instructors, you'll quickly gain the skills and knowledge needed to become a proficient Python programmer.</p><p>This opportunity will <strong>enhance your programming skills</strong> and take your career to the next level. Enrol in our course now and start your journey towards mastering Python programming!</p><p><strong>Learning Outcomes:</strong></p><p><strong>Learners who complete this course will be able to:</strong></p><ul><li>Design IT solutions.</li><li>Employ data structures and algorithms to address IT issues.</li><li>Use Python libraries to create computer programmes.</li><li>Gain expertise in Python code debugging and troubleshooting.</li><li>Combine Python with other technologies relevant to IT.</li><li>Apply Python to create and develop interactive IT applications.</li><li>Have a basic understanding of Python's object-oriented programming language.</li><li>Identify the proper design patterns and use them in IT projects.</li></ul><p><strong>Key Benefits:</strong></p><ul><li>Learning materials of the Python course contain engaging voiceover and visual elements for your comfort.</li><li>Get 24/7 access to all content for a full year.</li><li>Each of our students gets full tutor support on weekdays (Monday to Friday)</li></ul><p><strong><em>FREE Courses:</em></strong></p><ul><li>Clinical Data Management with SAS Programming</li><li>Higher Order Functions in Python</li><li>U&amp;P AI - Natural Language Processing (NLP) with Python</li><li>Machine Learning for Apps</li><li>Start a Web Design Business Step by Step Masterclass</li></ul><p><strong>Curriculum Breakdown: Python Programming</strong></p><ul><li><strong>Unit 01: </strong>Introduction to Python Programming</li><li><strong>Unit 02:</strong> Getting Started with Python</li><li><strong>Unit 03: </strong>Conditional Branching with Python</li><li><strong>Unit 04:</strong> Importing external/internal library in python</li><li><strong>Unit 05:</strong> Project Rock Paper and Scissors</li><li><strong>Unit 06: </strong>Strings Operation in Python</li><li><strong>Unit 07:</strong> Date and time in Python</li><li><strong>Unit 08: </strong>File Handling, read and write using Python</li><li><strong>Unit 09:</strong> Data Storage Structures, Tuple, List and Dictionary</li><li><strong>Unit 10:</strong> Writing user functions in Python</li><li><strong>Unit 11:</strong> Sending mail</li><li><strong>Unit 12:</strong> Import Tricks in Python</li><li><strong>Unit 13:</strong> Import Operating System and Platform</li><li><strong>Unit 14:</strong> Exceptions handling in python</li><li><strong>Unit 15:</strong> Installing Packages and Scheduling In Python</li><li><strong>Unit 16:</strong> Data Base In Python using SQLite</li><li><strong>Unit 17:</strong> Running Program from Command Prompt and Jupyter Notebook</li><li><strong>Unit 18:</strong> Conclusion</li></ul><p><strong>Additionally, you will receive additional informative modules from bonus courses.</strong></p><p><strong>Course Assessment </strong></p><p>To simplify the procedure of evaluation and accreditation for learners, we provide an automated assessment system. Upon completing an online module of Python, you will immediately be given access to a specifically crafted MCQ test. The results will be evaluated instantly, and the score will be displayed for your perusal. For each test, the pass mark will be set to 60%.</p><p><strong>Meet the Accreditation</strong></p><p>CPD Quality Standards (CPD QS) accreditation assures the Python course training and learning activities are relevant, reliable, and upto date.</p><p><strong>Expert Support</strong></p><p>Dedicated tutor support and 24/7 customer support are available to all students with this premium quality Python course.</p><p><strong>Exam &amp; Retakes:</strong></p><p>It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.</p><p><strong>Certification</strong></p><p>Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Anyone looking to start a career in data science</li><li>Professionals seeking to upskill in Python programming for data analysis</li><li>Students and recent graduates interested in pursuing data science as a career</li><li>Business owners and entrepreneurs looking to gain insights from data analysis</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Python course is open to all, with no formal entry requirements.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst (£31,000 - £50,000 per year)</li><li>Data Scientist (£40,000 - £70,000 per year)</li><li>Business Intelligence Analyst (£35,000 - £55,000 per year)</li><li>Machine Learning Engineer (£50,000 - £100,000 per year)</li><li>Data Engineer (£45,000 - £75,000 per year)</li><li>Data Consultant (£40,000 - £70,000 per year)</li><li>Big Data Engineer (£50,000 - £100,000 per year)</li><li>Data Architect (£70,000 - £120,000 per year)</li></ul>
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        <div>Certificates</div>
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                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
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            <div> <p><strong>CPD Accredited PDF Certificate.</strong><br />A PDF Certificate: £9</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p><strong>CPD Accredited Hard Copy Certificate. </strong><br />A Hardcopy Certificate (within the UK): £15<br />A Hardcopy Certificate (international): £15 + £10 (postal charge) = £25</p> </div>

                        </div>

Python Programming for Data Science Ultimate Bootcamp Course

<p>Data Science with Python bundle course teaches you everything on the Data Science with Python topic thoroughly from scratch so you can achieve a certificate for free to showcase your achievement in professional life.</p><p>This Data Science with Python bundle course is a comprehensive course designed to provide a detailed understanding of the nature of the Data Science with Python related sector and your key roles within it.</p><p>The training materials of this Data Science with Python course are available online for you to learn at your own pace and fast-track your career with ease.</p><p><strong>Key Features of </strong><strong>Data Science with Python </strong><strong>Bundle</strong></p><ul><li>CPD Accredited Data Science with Python Course</li><li>Instant PDF certificate</li><li>Fully online, interactive Data Science with Pythoncourse</li><li>Self-paced learning and laptop, tablet, smartphone-friendly</li><li>24/7 Learning Assistance</li><li>Discounts on bulk purchases</li></ul><p><strong>Enrol now in this </strong><strong>Data Science with Python</strong><strong> Bundle course to excel! </strong></p><p>To become successful in your profession, you must have a specific set of Data Science with Python skills to succeed in today's competitive world. In this in-depth Data Science with Pythontraining course, you will develop the most in-demand Data Science with Python skills to kickstart your career, as well as upgrade your existing knowledge &amp; skills.</p><p><strong>Data Science with Python </strong><strong>Curriculum</strong></p><ul><li><strong>Course 01:</strong> Data Analytics</li><li><strong>Course 02:</strong> Python Programming: Beginner To Expert</li><li><strong>Course 03:</strong> Complete Java</li><li><strong>Course 04:</strong> Machine Learning Basics</li></ul><p><strong>Accreditation</strong></p><p>This Data Science with Python bundle courses are CPD accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field.</p><p><strong>Certification</strong></p><p>Once you've successfully completed your Data Science with Python course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99).</p><h2>
        
        <div>CPD</div>
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                        <div id="cpd">
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                                        50 CPD hours / points
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                                            Accredited by CPD Quality Standards
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                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This course is ideal for all employees or anyone who genuinely wishes to learn more about Data Science with Python basics.</p>
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        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No prior degree or experience is required to enrol in this course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This Data Science with Python Course will help you to explore avariety of career paths in the related industry.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Digital certificate</h4>
        <p>
            Digital certificate -
            Included
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        <h4>Hardcopy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p><strong>Hardcopy Certificate (UK Delivery):</strong></p><p>For those who wish to have a physical token of their achievement, we offer a high-quality, printed certificate. This hardcopy certificate is also provided free of charge. However, please note that delivery fees apply. If your shipping address is within the United Kingdom, the delivery fee will be only <strong>£3.99</strong>.</p><p><strong>Hardcopy Certificate (International Delivery):</strong></p><p>For all international addresses outside of the United Kingdom, the delivery fee for a hardcopy certificate will be only <strong>£10</strong>.</p></div>

                        </div>

Training Express

Data Science with Python Course

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

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

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

Python

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

Machine Learning (ML)

Machine Learning for Apps Level 4

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

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

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

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

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

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

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

Python Machine Learning Crash Course for Beginners

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

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

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

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

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

The code bundle for this course is available at https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2</p>
        <h2>What You Will Learn</h2><p>Understand how to use Jupyter Lab for Jupyter notebooks<br>Learn Python fundamentals<br>Load large datasets from files using pandas<br>Uncover techniques to perform data analysis and exploration<br>Group, sort, export, select, filter, and clean data<br>Find out how to preview the data frame</p>
        <h2>Audience</h2><p>This course is designed for beginners looking to understand the theoretical side of machine learning and to enter the practical side of data science. You will understand the fundamentals of Python and learn how to pre-process data before using it in machine learning algorithms. There are no prerequisites for taking up this course; however, it is recommended to finish with the Machine Learning for Absolute Beginners - Level 1 course.</p>
        <h2>Approach</h2><p>With the help of exercises and activities, this course will show you how to use Python and pandas library for cleaning data before moving it into machine learning algorithms.</p>
        <h2>Key Features</h2><p>Learn how to use Python for data science and machine learning projects * Understand the interface of Jupyter lab tool * Discover how to get data ready for machine learning algorithms</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Machine-Learning-for-Absolute-Beginners-Level-2 </p>
        <h2>About the Author</h2><h3>Idan Gabrieli</h3><p>Idan Gabrieli has worked in various engineering positions in Israel's high-tech industry. Idan has gained extensive experience with hundreds of business companies, transforming their challenges and opportunities into practical use cases and leveraging cutting-edge technologies.

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

Idan simplifies complex technology and provides high-quality educational content with specific learning objectives that are well-structured, combining various multimedia teaching options.</p>
        <h2>Course Outline</h2><h4>1. Getting Started with Level 2!</h4><p></p><br><h4>2. Python Fundamentals for Data Science</h4><p></p><br><h4>3. Introduction to the Pandas Library</h4><p></p><br><h4>4. Loading Data into a DataFrame</h4><p></p><br><h4>5. Data Cleaning and Transformation</h4><p></p><br><h4>6. Course Summary</h4><p></p>

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

Machine Learning for Absolute Beginners - Level 2

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

TensorFlow

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

The Complete Machine Learning Course with Python

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					<div><p>The comprehensive Machine Learning - Use in Flutter has been designed by industry experts to provide learners with everything they need to enhance their skills and knowledge in their chosen area of study. Enrol on the Machine Learning - Use in Flutter today, and learn from the very best the industry has to offer!</p></div>
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					<div><p>This best selling Machine Learning - Use in Flutter has been developed by industry professionals and has already been completed by hundreds of satisfied students. This in-depth Machine Learning - Use in Flutter is suitable for anyone who wants to build their professional skill set and improve their expert knowledge.</p><p>The Machine Learning - Use in Flutter is CPD-accredited, so you can be confident you're completing a quality training course which will boost your CV and enhance your career potential. The Machine Learning - Use in Flutter is made up of several information-packed modules which break down each topic into bite-sized chunks to ensure you understand and retain everything you learn.</p><p>After successfully completing the Machine Learning - Use in Flutter, you will be awarded a certificate of completion as proof of your new skills. If you are looking to pursue a new career and want to build your professional skills to excel in your chosen field, the certificate of completion from the Machine Learning - Use in Flutter will help you stand out from the crowd. You can also validate your certification on our website.</p><p>We know that you are busy and that time is precious, so we have designed the Machine Learning - Use in Flutter to be completed at your own pace, whether that's part-time or full-time. Get full course access upon registration and access the course materials from anywhere in the world, at any time, from any internet-enabled device. </p><p>Our experienced tutors are here to support you through the entire learning process and answer any queries you may have via email.</p></div>
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Learning & Development

Learning To Learn

Machine Learning - Use in Flutter

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

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

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

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

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

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

Deep Learning

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

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