Name: Software Engineering: Coding & Web Development - CPD Certified
Price: 109 GBP
Availability: InStock

<strong>You will master the fundamentals and more advanced principles of Python programming in this beginner's tutorial. This Python course covers every Python fundamental, including installation and more complex material such as Python data science.</strong>
        <p>'Python is the future of artificial intelligence and machine learning.'-Adrian Rosebrock, author.

Python is the programming language most loved by developers, data scientists, software engineers, and even hackers.

This Python basic to advanced course covers the fundamental concepts of Python programming and ways to apply this to real-world applications. The course modules, lesson-end quizzes, and lesson-end projects collectively cover the training course topics such as data operations, strings, file I/O, exception handling, error handling, conditional statements, CGI programming, shell scripting, web scraping, and Django.

It also covers Logging Framework, Unittest Framework, Socket Programming, Tornado Framework, and Networking. You will also build a Python chat server.

By the end of this course, you will have the skills to create and send robust Python applications using off-the-rack libraries confidently.

The code bundle for this course is available at: https://github.com/PacktPublishing/Python-Programming</p>
        <h2>What You Will Learn</h2><p>Learn the basics of Python<br>Learn networking and database access using Python and CGI programming<br>Understand the Python debugger<br>Understand web scraping using Beautiful Soup<br>Learn about Python Unittest<br>Build a Python chat server</p>
        <h2>Audience</h2><p>This course is for new Python users looking to make the best of this powerful programming language-college freshers, software developers, programming enthusiasts, and technical leads. Python is widely used in artificial intelligence, so if you are planning to start your career in AI/machine learning then you need to have an understanding of Python as a prerequisite.</p>
        <h2>Approach</h2><p>This course comprises self-paced videos, demos, lesson-end quizzes, and a competitive course-end project. This will help you increase your in-depth understanding of Python's design, memory models, object-arranged highlights, and information, and be industry-prepared.</p>
        <h2>Key Features</h2><p>Go from a beginner level to a confident level of Python programming * Increase your in-depth understanding of Python's design, memory models, and more * Work on a competitive project at the end of the course</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/python-programming</p>
        <h2>About the Author</h2><h3>Learnkart Technology Private Limited</h3><p>Learnkart understands the importance of upskilling and its impact on you. They pride themselves in creating specialized e-learning courses that will not only prepare you for certification exams but also help you gain hands-on knowledge for real-world applications. Thousands of students have developed their skills with Learnkart through a variety of courses, such as PgMP, Risk Management Professional (RMP), speech recognition, Python, Amazon Web Services (AWS), and more. Learnkart regularly adds courses to its portfolio to lend more value to your learning experience.</p>
        <h2>Course Outline</h2><h4>1. Course Introduction</h4><p></p><br><h4>2. Python Basics</h4><p></p><br><h4>3. Data Operations</h4><p></p><br><h4>4. Conditional Statements and Functions</h4><p></p><br><h4>5. Error Handling and File Operations</h4><p></p><br><h4>6. Shell Scripting and Django</h4><p></p><br><h4>7. Course End Project</h4><p></p>

Packt

Python

Programming

You will master the fundamentals and more advanced principles of Python programming in this beginner's tutorial. This Python course covers every Python fundamental, including installation and more complex material such as Python data science.

Python Programming

General Admission

Java

Android Development

Mobile

Kotlin

Android

Learn new Android APIs such as RoomDatabase, ML Kit for face recognition, Cloud Firestore, Firebase, Maps, and the Android Studio IDE (integrated development environment)

The Comprehensive Android Developer Bootcamp

<strong>Looking to build an AI application from scratch? Look no further than this compact course with ChatGPT! Using the OpenAI API and the latest web development technologies, including React, Next.js, JavaScript, Node, and CSS, you will gain hands-on experience building an AI-powered application that generates pet names for users.</strong>
        <p>ChatGPT is not a replacement, but an enhancing tool and an incredibly powerful tool when it comes to programming and analyzing code, and ignoring the utility now or at the very least not giving it a proper test run could very well be missing out early on gaining the advantage. The primary focus of the course is to equip individuals with the knowledge and skills they need to create a fully functional AI-powered application that generates pet names for users.

The course starts with introducing you to ChatGPT, the latest web development tools, and installing the required tools. You will gain hands-on experience with web development technologies, implement machine learning concepts in a real-world project, and gain a deeper understanding of machine learning concepts. The course is structured in a way that enables you to gain hands-on experience with React Hooks, which are essential for building interactive interfaces for ChatGPT-style applications. You will also see how to use Fetch to make API calls to OpenAI and NPX to run Node commands.

By the end of the course, you will have gained practical experience in building an AI-powered application, using machine learning concepts, and creating interactive interfaces with React Hooks. You will have a fully functional pet name generator application to add to your portfolio.

 All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/Building-a-ChatGPT-AI-with-JavaScript---An-OpenAI-Code-Along-Guide</p>
        <h2>What You Will Learn</h2><p>Build an AI application using the OpenAI API and web development technologies<br>Download and install the required tools along with dependencies<br>Get hands-on experience with React, Next.js, JavaScript, Node, and CSS<br>Use Fetch to make API calls to OpenAI and NPX to run Node commands<br>Implement machine learning concepts in a real-world project<br>Use React Hooks and build interactive interfaces for the AI application</p>
        <h2>Audience</h2><p>This course is ideal for those who are interested in modern web development technology and want to gain confidence as a developer for jobs with ChatGPT. It is also suitable for those who already have basic JavaScript knowledge and want to learn more about expert topics. If you are interested in exploring the use cases of ChatGPT and want to get started with programming, this course is for you. Additionally, if you are interested in working on a ChatGPT project in the future, this course is a great starting point.<br><br>No programming experience needed. You will learn everything you need to know inside. Basic JavaScript and web development is a plus!</p>
        <h2>Approach</h2><p>This course takes a concise and focused approach to teaching the skills necessary to build an AI-powered application using the OpenAI API and web development technologies such as React, Next.js, JavaScript, Node, and CSS.<br><br>The course engages learners in a fun and interactive environment, with short, crisp, and hands-on content.</p>
        <h2>Key Features</h2><p>Create a functional and practical AI-powered application that can generate pet names for users * Apply and implement machine learning concepts to real-world projects and developing your own AI-powered projects * Highly practical and actionable content that you can immediately apply to your work or personal life</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Building-a-ChatGPT-AI-with-JavaScript---An-OpenAI-Code-Along-Guide</p>
        <h2>About the Author</h2><h3>Clarian North</h3><p>Clarian North is a recognized industry-certified instructor, CEO of Orbital LLC, and a senior project engineer with over 90K enrolments worldwide. His students have gone on to work for some of the biggest production companies such as Universal, Google, Amazon, Warner, and the BBC. He has contributed to viral streams, grown a unique digital imprint in over 80 countries, and been featured in Rolling Stone, Billboard, Guardian, Bloomberg, and Vice.

He has worked for majors, indies, and international publishing houses, including Grammy-nominated and award-winning productions, mixing for Emirates Airline Inflight Radio, and producing for Kompakt Records. He is dedicated to giving accessible and comprehensive tech courses, teaching complex subject matter with clear and example-based explanations, and guiding students through complex techniques with just a computer and free software.</p>
        <h2>Course Outline</h2><h4>1. Setting Up the OpenAI ChatGPT Project</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction to the Course</p><p style='margin: 0'>This video helps in providing an overview of what the course entails and what students can expect to learn.</p></td></tr><tr><td style='padding: 15px;'><p>2. Going Through the Final ChatGPT AI Model</p><p style='margin: 0'>In this video, we will demonstrate the end results of building a ChatGPT AI with JavaScript using OpenAI, showcasing the chatbot in action.</p></td></tr><tr><td style='padding: 15px;'><p>3. What Is Next.js and React.js</p><p style='margin: 0'>In this video, you will learn about Next.js and React.js and their fundamental concepts in detail.</p></td></tr><tr><td style='padding: 15px;'><p>4. What Is Node.js and NPM - OpenAI Project Setup</p><p style='margin: 0'>This video demonstrates and explains the Node.js and NPM. Here, you will look at the project setup of OpenAI.</p></td></tr><tr><td style='padding: 15px;'><p>5. Install Next.js with NPX - ChatGPT Project Setup</p><p style='margin: 0'>This video helps you with the installation of Next.js with NPX. Here, you will look at setting up the ChatGPT project.</p></td></tr><tr><td style='padding: 15px;'><p>6. Download and Install a Text Editor - Visual Studio Code for OpenAI ChatGPT</p><p style='margin: 0'>In this video, we will guide you through the process of downloading and installing Visual Studio Code, a popular text editor, to use with the OpenAI ChatGPT project.</p></td></tr><tr><td style='padding: 15px;'><p>7. How Does NPM Run Dev Work in Next.js</p><p style='margin: 0'>In this video, we will explore the process of running the "npm run dev" command in Next.js and how it helps facilitate a smooth development experience for building ChatGPT OpenAI projects.</p></td></tr><tr><td style='padding: 15px;'><p>8. Complete File Structure Review of the OpenAI ChatGPT</p><p style='margin: 0'>In this video, we will do a complete file structure review of the OpenAI ChatGPT project and discuss the purpose of each file and directory.</p></td></tr></tbody></table><br><h4>2. Client-Side Code for OpenAI ChatGPT</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Overview of Next.js Key Index and Starter Coding Files for the ChatGPT OpenAI</p><p style='margin: 0'>In this video, we will provide an overview of the key index and starter coding files for the ChatGPT OpenAI application built with Next.js.</p></td></tr><tr><td style='padding: 15px;'><p>2. Cleaning Up the Index File for ChatGPT OpenAI API</p><p style='margin: 0'>In this video, we will clean up the index file for our ChatGPT OpenAI API by removing redundant code and adding comments to make it easier to understand.</p></td></tr><tr><td style='padding: 15px;'><p>3. Writing a Form in React with Next.js for ChatGPT OpenAI</p><p style='margin: 0'>This video covers the process of writing a form in React with Next.js for integrating ChatGPT OpenAI into a web application.</p></td></tr><tr><td style='padding: 15px;'><p>4. What are Hooks and How to Manage State in React</p><p style='margin: 0'>This video covers an introduction to React hooks and how to manage state in React, which is essential for building complex applications like the ChatGPT OpenAI.</p></td></tr></tbody></table><br><h4>3. Client-Side Adding State and Hooks to the OpenAI ChatGPT Application</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Animal Input State Update for Open AI Queries</p><p style='margin: 0'>In this video, we will demonstrate how to implement an animal input state update for OpenAI queries, allowing your chatbot to provide more personalized and engaging responses for animal-related topics.</p></td></tr><tr><td style='padding: 15px;'><p>2. onSubmit Functionality for OpenAI API Connection with ChatGPT</p><p style='margin: 0'>In this video, you will learn how to add onSubmit functionality for OpenAI API connection with ChatGPT, enabling your application to query the OpenAI API and display responses based on user input.</p></td></tr><tr><td style='padding: 15px;'><p>3. Async Await Fetch OpenAI and ChatGPT Post Request Pattern</p><p style='margin: 0'>This video demonstrates the use of async/await and fetch to send a post request to the OpenAI API, allowing ChatGPT to respond to user input in real time.</p></td></tr><tr><td style='padding: 15px;'><p>4. Async Await Fetch OpenAI and ChatGPT Data Response Pattern</p><p style='margin: 0'>In this video, you will see how async/await and fetch can be used to fetch data from the OpenAI API and display the responses in a ChatGPT application. You will also learn about data response patterns and how they can be used to manipulate the data returned by the API.</p></td></tr></tbody></table><br><h4>4. OpenAI API Setup and Documentation</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. OpenAI API ChatGPT Examples</p><p style='margin: 0'>This video showcases examples of using OpenAI API with ChatGPT for generating text, answering questions, and creating chatbots.</p></td></tr><tr><td style='padding: 15px;'><p>2. ChatGPT API Key for OpenAI and Environment Security Protocol</p><p style='margin: 0'>In this video, you will learn about ChatGPT API key for OpenAI and how to ensure environment security protocol to protect your system from potential security breaches.</p></td></tr></tbody></table><br><h4>5. Finish Styling the OpenAI Application</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. CSS Styling to OpenAI Form</p><p style='margin: 0'>In this video, you will learn how to apply CSS styling to an OpenAI form and make it look more visually appealing for your users.</p></td></tr><tr><td style='padding: 15px;'><p>2. Add Result Styling CSS to OpenAI Project</p><p style='margin: 0'>In this video, we will guide you on how to add result styling CSS to your OpenAI project to make the output more visually appealing for your users.</p></td></tr></tbody></table><br><h4>6. Generate API for OpenAI Configuration with ChatGPT</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Call and Response Example for OpenAI Implementation and Customizations</p><p style='margin: 0'>In this video, we will provide a call and response example for OpenAI implementation and customizations, demonstrating how to optimize and personalize your chatbot's responses.</p></td></tr><tr><td style='padding: 15px;'><p>2. Robust Error Handling and Logic Completion to OpenAI API</p><p style='margin: 0'>In this video, we will teach you how to implement robust error handling and logic completion for your OpenAI API, ensuring that your application can handle unexpected user input and provide accurate responses.</p></td></tr></tbody></table>

JavaScript

Node.js

Next.js

ChatGPT

Looking to build an AI application from scratch? Look no further than this compact course with ChatGPT! Using the OpenAI API and the latest web development technologies, including React, Next.js, JavaScript, Node, and CSS, you will gain hands-on experience building an AI-powered application that generates pet names for users.

Building a ChatGPT AI with JavaScript - An OpenAI Code-Along Guide

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Get ready for an exceptional online learning experience with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Analyst (Data Analytics)</strong>bundle! This carefully curated collection of<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> 30 premium courses</strong> is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The Data Analyst (Analytics) is a dynamic package, blending the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you&#39;re looking to break into a new field or deepen your existing knowledge, the<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;"> Data Analyst (Analytics) </strong>package has something for everyone.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">As part of the Data Analyst (Analytics) package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Data Analyst (Analytics) bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">This Bundle Comprises the Following </strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Analyst (Data Analytics)</strong><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">CPD Accredited Courses:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 01</strong>: Basic Data Analysis</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 02</strong>: Business Data Analysis</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 03</strong>: Introduction to Data Analytics with Tableau</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 04</strong>: Google Data Studio: Data Analytics</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 05</strong>: SQL Database Basics for Everyone</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 06</strong>: R Programming for Data Science</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 07</strong>: 2021 Data Science &amp; Machine Learning with R from A-Z</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 08</strong>: Learn Python, JavaScript, and Microsoft SQL for Data science</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 09</strong>: Spatial Data Visualisation and Machine Learning in Python</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 10</strong>: Building Big Data Pipelines with PySpark MongoDB and Bokeh</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 11</strong>: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 12</strong>: Clinical Data Management with SAS Programming</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 13</strong>: Certificate in Data Entry and Management</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 14</strong>: Quick Data Science Approach from Scratch</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 15</strong>: Web Mapping and Data Visualizations</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 16</strong>: Programming AutoCAD with SQL Server Database Using C#</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 17</strong>: Big Data Analytics with PySpark Power BI and MongoDB</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 18</strong>: Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 19</strong>: Develop Big Data Pipelines with R, Sparklyr &amp; Power BI</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 20</strong>: Data Center Training Essentials: Power &amp; Electrical</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 21</strong>: Business Intelligence and Data Mining</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 22</strong>: Set Menu Prices for your restaurant using data</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 23</strong>: Data Analysis In Excel</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 24</strong>: Data Protection</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 25</strong>: Reporting and Data</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 26</strong>: Career Development Plan Fundamentals</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 27</strong>: CV Writing and Job Searching</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 28</strong>: Networking Skills for Personal Success</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 29</strong>: Excel: Top 50 Microsoft Excel Formulas in 50 Minutes!</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 30</strong>: Decision Making and Critical Thinking</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">What will make you stand out?</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Upon completion of this online Data Analyst (Data Analytics) bundle, you will gain the following:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">CPD QS Accredited Proficiency with this  <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Analyst (Analytics)</strong> bundle </li><li fr-original-style="" style="box-sizing: border-box;">After successfully completing the Data Analyst (Analytics) &nbsp;bundle, you will receive a FREE CPD PDF Certificates as evidence of your newly acquired abilities.</li><li fr-original-style="" style="box-sizing: border-box;">Lifetime access to the whole collection of learning materials of this <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Analyst (Analytics)</strong> bundle</li><li fr-original-style="" style="box-sizing: border-box;">The online test with immediate results </li><li fr-original-style="" style="box-sizing: border-box;">You can study and complete the Data Analyst (Analytics) bundle at your own pace.</li><li fr-original-style="" style="box-sizing: border-box;">Study for the Data Analyst (Analytics) bundle using any internet-connected device, such as a computer, tablet, or mobile device.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Each course in this Data Analyst (Analytics) bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Embrace the future of learning with the Data Analyst (Analytics), a rich anthology of 30 diverse courses. Each course in the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Analyst (Analytics)</strong> bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. ThisData Analyst (Analytics) bundle will take you on a unique and enriching educational journey.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Data Analyst (Analytics) bundle offers you the flexibility and convenience to learn at your own pace. Make the Data Analyst (Data Analytics) package your trusted companion in your lifelong learning journey.</p><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">CPD</div>
    </h2>

                        <div id="cpd" fr-original-style="" style="box-sizing: border-box;">
                            <div fr-original-style="" style="box-sizing: border-box;">
                                <div fr-original-style="" style="box-sizing: border-box;">
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                                        300 CPD hours / points
                                    </div>
                                        <div fr-original-style="" style="box-sizing: border-box;">
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Analyst (Data Analytics)</strong> bundle is perfect for:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Lifelong learners looking to expand their knowledge and skills.</li><li fr-original-style="" style="box-sizing: border-box;">Professionals seeking to enhance their career with CPD certification.</li><li fr-original-style="" style="box-sizing: border-box;">Individuals wanting to explore new fields and disciplines.</li><li fr-original-style="" style="box-sizing: border-box;">Anyone who values flexible, self-paced learning from the comfort of home.</li></ul>
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        <div fr-original-style="" style="box-sizing: border-box;">Requirements</div>
    </h2>

                        <div id="requirements" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">You are cordially invited to enroll in this bundle; please note that there are no formal prerequisites or qualifications required. We&#39;ve designed this curriculum to be accessible to all, irrespective of prior experience or educational background.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Career path</div>
    </h2>

                        <div id="career_path" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Unleash your potential with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Analyst (Data Analytics) </strong>bundle. Acquire versatile skills across multiple fields, foster problem-solving abilities, and stay ahead of industry trends. Ideal for those seeking career advancement, a new professional path, or personal growth. Embrace the journey with the <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Data Analyst (Analytics)</strong>bundle package.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Certificates</div>
    </h2>

                        <div id="optional_extras" fr-original-style="" style="box-sizing: border-box;">
                                    <h4 fr-original-style="" style="box-sizing: border-box;">CPD Quality Standard Certificate</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Digital certificate -
            Included
        </p>
            <div fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">30 CPD Quality Standard Certificates - Free</p></div>

                        </div>

NextGen Learning

Business Analytics & Intelligence

Big Data Analytics

Data Science

Data Skills

Data Analyst

Continuous Professional Development (CPD)

Data Analyst (Data Analytics) - 30 CPD Courses!

<section>
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			<div></div>
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					<div><p>Ever dreamt of navigating the intricate pathways of the human brain's counterparts in the machine world? Welcome to the 'Deep Learning &amp; Neural Networks Python - Keras' course, an odyssey that paints a vivid picture of the intertwining webs of neural networks. Our meticulously crafted modules transport you from the foundational stones of Python basics straight into the high towers of Theano and TensorFlow library installations.</p><p>Transition smoothly from understanding the complex labyrinths of neural structures to implementing them with flair. Whether it's the Pima Indian Diabetes or the Iris Flower Multi-Class models, we'll unravel the steps, terminologies, and techniques with precision. Dive deeper into the shimmering waters of Convolutional Neural Networks, where you'll gain first-hand knowledge on data giants like the MNIST Handwritten Digit Recognition and the CIFAR-10 Object Recognition Datasets.</p><p>As the cherry on top, our Deep Learning &amp; Neural Networks Python course does not merely offer theoretical insights. Instead, we illuminate the path to applying these neural structures in tangible scenarios. From checkpointing neural network improvements to plotting model behavior history and understanding image augmentation, every corner you turn unveils another layer of deep learning wonder. Join us in this transformative journey and sculpt your future in the AI realm.</p></div>
				</div>
				</div>
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				<section>
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			<h2><b> Learning outcome</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Differentiate between machine learning and deep learning in AI projects.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Navigate through Python basics and library installations including Theano and TensorFlow.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Develop and evaluate models including Pima Indian Diabetes, Iris Flower Multi-Class, and more.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Gain proficiency in convolutional neural networks with insights on MNIST and CIFAR-10 Datasets.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Apply knowledge in practical scenarios by loading and predicting various models.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Understand the importance and application of dropout regularization and learning rate schedules.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Effectively use checkpointing to improve and save neural network models.</span>
									</li>
						</ul>
				</div>
				</div>
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			</div>
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			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2> <b>Why should I take this course?</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Comprehensive coverage of deep learning and neural networks.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Step-by-step guide on developing and evaluating various models.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Access to real datasets like MNIST and CIFAR-10.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Insights on advanced concepts like dropout regularization and learning rate schedules.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Learn how to use checkpointing to improve neural network models.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2> <b>Career Path</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Data Scientist.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Machine Learning Engineer.</span>
									</li>
								<li>
											<span>
													</span>
										<span>AI Research Scientist.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Neural Network Developer.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Deep Learning Specialist.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Computer Vision Engineer.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>
				<section>
						<div>
				<div>
				<div>
			<div>
					<div>
				<div>
				<div>
			<h2> <b>Requirements</b></h2>		</div>
				</div>
				<div>
				<div>
					<ul>
							<li>
											<span>
													</span>
										<span>Basic understanding of Python programming.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Basic knowledge of machine learning concepts.</span>
									</li>
								<li>
											<span>
													</span>
										<span>Willingness to learn and apply new skills in deep learning.</span>
									</li>
						</ul>
				</div>
				</div>
						</div>
			</div>
		</div>
						</div>
			</div>
		</section>

Skill Up

Let your AI journey transcend new heights with our Deep Learning & Neural Networks Python - Keras course, your best path to mastering deep learning.

Deep Learning & Neural Networks Python - Keras

Medway

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

futureCoders SE

Data Storytelling

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

Introduction to Data Science

General admission

<p><strong>Earn up to 85k as a 'Cyber Security Analyst' or Your 100% Money Back</strong></p><p>Are you concerned about the rising threat of cybercrime? In the UK alone, cyberattacks cost businesses an estimated<strong> £3.1 billion</strong> annually, and the demand for skilled cybersecurity professionals is booming. With an average salary of <strong>£45,000,</strong> a career in cybersecurity offers promising opportunities for those seeking stability and growth. Are you looking to join the high-demand field of cyber security and earn a handsome salary? Our Network and Cyber Security Traineeship Program is here to guide you all the way until you get the job you want.</p><p>This Network and Cyber Security Traineeship Program is your <strong>all-in-one solution</strong> to learn cyber security from scratch and also <strong>secure a job</strong> in this exciting industry. With our unique blend of comprehensive courses and career support, your dream job is within reach. We understand the challenges of transitioning into a new field, which is why our program is tailored to guide you every step of the way - from mastering core skills to landing your first job as a Cyber Security Analyst. By choosing our Cyber Security Job Guarantee Programme, you're not just gaining skills; <strong>you're gaining a </strong><strong>lifelong career partner</strong>.</p><p><strong>Our mission is simple</strong> - to be your trusted partner every step of the way, from training to employment.</p><p>In addition to teaching you the technical skills you need, we will also provide you with <strong>career mentoring and support</strong>. We will help you build your resume, prepare for interviews, and land your <strong>dream job</strong>. We also have<strong> partnerships with many companies</strong> that are hiring Programmers, so we can help you get your foot in the door.</p><p>If you are not happy with our service, we also offer a <strong>100% money-back guarantee</strong>. So what are you waiting for? Enrol in our Cyber Security Job Guarantee Programme today and start your journey to become successful in Cyber Security field!</p><p><strong>Why Choose Us?</strong></p><p>So, what sets us apart from other programs? Let's dive into the exceptional benefits you'll experience when you join our Certified Cyber Security Job Guarantee Programme:</p><ul><li><strong>Personalised Guidance:</strong> We believe in the power of individual attention. Our experienced mentors will provide one-on-one counselling sessions tailored to your specific needs. Whether you're a beginner or have some Cyber Security experience, we will guide you towards honing your skills and developing a strong foundation in Cyber Security.<br /><br /></li><li><strong>One-On-One Consultation:</strong> Gain invaluable insights and guidance from seasoned professionals who have thrived in the Cyber Security field. Our consultation sessions provide you with insider tips, tricks, and advice, empowering you to navigate the industry with confidence and expertise.<br /><br /></li><li><strong>Extensive Job Opportunities:</strong> We have established partnerships with numerous companies actively seeking Certified Cyber Security Job Guarantee Programme. Through our network, we'll connect you with exclusive job openings that are not easily accessible elsewhere.<br /><br /></li><li><strong>Interview Preparation:</strong> No more stress over unexpected interview questions. We provide you with access to a comprehensive database of potential interview questions curated over years of industry experience. Walk into your interviews confident, well-prepared, and ready to impress.</li></ul><p><strong>Courses we will provide once you enrol in the program:</strong></p><ul><li><strong>Course 01: </strong>Cyber Security Programming</li><li><strong>Course 02: </strong>Cyber Security Advanced Training</li><li><strong>Course 03: </strong>Cyber Security Incident Handling and Incident Response</li><li><strong>Course 04: </strong>IT Support Technician</li><li><strong>Course 05: </strong>General Data Protection Regulation (GDPR) &amp; Cyber Security Management</li><li><strong>Course 06: </strong>CompTIA Security+ (SY0-601)</li><li><strong>Course 07: </strong>CompTIA A+ (220-1001)</li><li><strong>Course 08: </strong>CompTIA A+ (220-1002)</li><li><strong>Course 09: </strong>Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li><strong>Course 10: </strong>CompTIA CySA+ Cybersecurity Analyst (CS0-002)</li><li><strong>Course 11: </strong>CompTIA IT Fundamentals ITF+ (FCO-U61)</li><li><strong>Course 12: </strong>CompTIA Network+ Certification (N10-007)</li><li><strong>Course 13: </strong>Diploma in CompTIA Network+ Training at QLS Level 7</li><li><strong>Course 14: </strong>CompTIA PenTest+ (Ethical Hacking)</li><li><strong>Course 15: </strong>Cisco ASA Firewall Training</li><li><strong>Course 16: </strong>Cryptography</li><li><strong>Course 17: </strong>IT Administration and Networking</li><li><strong>Course 18: </strong>IGCSE ICT: Network Essentials</li><li><strong>Course 19: </strong>Cyber Security Law</li><li><strong>Course 20: </strong>Cyber Security Awareness Training</li></ul><p><strong>How Can We Offer Job Guarantees?</strong></p><p>HF Online provides consultancy, professional and educational services to many of the companies in the UK. During our intense exclusive training program, you will not just gain and complete the industry valuable certification but will gain industry experience as well, which is imperative to get your 1st job in the sector.</p> <p><strong>The Network and Cyber Security Traineeship Program Programme is</strong><strong> completed in 9 easy steps:</strong></p><p><strong>Step 1: Enrol in the Programme</strong></p><p>Begin your exciting journey with us by enrolling in the Cyber Security Job Guarantee Programme. Complete your registration and make a secure online payment. Remember, we offer a 14-day money-back guarantee if you're not completely satisfied.</p><p>After you enrol in the Program, you will get lifetime access to<strong> 20 Premium Courses</strong> related to Cyber Security Job Guarantee Programme. These courses will teach you the knowledge and skills required to become successful in Cyber Security field. Our customer service team will help you and keep in contact with you every step of the way. So you won't have to worry about a thing!</p><p><strong>Step 2: Initial One-On-One Counselling Session</strong></p><p>Once enrolled, you will be paired with a dedicated career mentor. Schedule your first one-on-one session to discuss your career aspirations, skills, experience, and any areas for potential growth. This conversation will shape your learning and development path.</p><p><strong>Step 3 - </strong><strong>Certification upon Course Completion</strong></p><p>After learning from the courses, you must obtain certificates for each course. There will be exams for every course, and you have to pass them to get your certificate. To pass successfully, you must get 90% marks. Once you pass the exams, you will receive hardcopy certificates. These certificates will prove that you're an expert in the subject.</p><p><strong>Step 4: </strong><strong>CV Revamping</strong></p><p>Our team of professionals will build you a compelling CV and <strong>LinkedIn profile</strong>. We'll ensure it presents your skills and qualifications effectively and is tailored to the needs and expectations of the programming/development industry. With these powerful tools in hand, you'll be fully prepared to tackle job interviews confidently.</p><p><strong>Step 5: Building Network and Submitting CV</strong></p><p>We understand the power of casting a wide net. We'll strategically submit your CV to various platforms and networks, expanding your reach and connecting you with valuable opportunities that align with your career goals. We will also make connections with many<strong> high-profile individuals and companies</strong> through your LinkedIn profile.</p><p><strong>Step 6: Interview Preparation</strong></p><p>With your CV ready, we'll move on to interview preparation. Gain exclusive access to our database of potential interview questions. Through simulated interviews with your mentor, you'll practice your responses and receive valuable feedback to further refine your skills.</p><p><strong>Step 7: Securing Job Interviews</strong></p><p>Leveraging our partnerships with leading companies, <strong>we'll secure job interviews for you</strong>. We'll ensure you get the opportunity to showcase your skills to potential employers and <strong>get the dream job you want</strong>.</p><p><strong>Step 8: Post-Interview Support</strong></p><p>Post-interview, we'll provide a debriefing session to reflect on your performance and identify areas of improvement for future interviews if necessary. Remember, our commitment extends until you land your dream job.</p><p><strong>Step 9: Celebrate Your New Job!</strong></p><p>Once you've secured your dream job in Cyber Security, it's time to celebrate! However, our support doesn't end there. We'll provide you with ongoing career advice to ensure you continue to thrive in your new role.</p><p>We're excited to accompany you on this journey to success. Enrol today, and let's get started! Your path to a successful career in Cyber Security field begins with us.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        1000 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 Cyber Security Job Guarantee Programme Course is suitable for:</p><ul><li>Beginners with no prior programming experience who are looking to enter the world of coding.</li><li>Intermediate programmers who wish to expand their skills and explore Cyber Security's extensive usage.</li><li>IT professionals seeking to diversify their skill sets and stay relevant in today's tech-driven market.</li><li>Data analysts and scientists who want to leverage Cyber Security's powerful libraries for their data processing needs.</li><li>Students and recent graduates looking to enhance their employability in the thriving tech industry.</li><li>Entrepreneurs who wish to understand and utilise Cyber Security to automate tasks and analyse data for their businesses.</li><li>Anyone with a keen interest in Cyber Security and a desire to delve into its applications in diverse fields like web development, data science, machine learning, and more.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No experience required. Just enrol &amp; start learning.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Cyber Security Analyst: £35,000 - £85,000 annually</li><li>Incident Response Specialist: £40,000 - £90,000 annually</li><li>IT Support Technician: £25,000 - £55,000 annually</li><li>GDPR Compliance Officer: £30,000 - £70,000 annually</li><li>Cloud Security Architect: £50,000 - £120,000 annually</li><li>Ethical Hacker/Penetration Tester: £35,000 - £95,000 annually</li><li>Network Security Engineer: £40,000 - £100,000 annually</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited e-Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited Framed (Hardcopy) Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Enrolment Letter</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>QLS Endorsed Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Academy for Health and Fitness

Certified in Cybersecurity (CC)

Certificate in Information Security Management Principles (CISMP)

Cyber Security

Cyber Security Foundation

Certified Cyber Professional (CCP)

CompTIA Security+

Network and Cyber Security Traineeship Program with Career Support & Money Back Guarantee

<p><strong>Get Hard Copy + PDF Certificates + Transcript + Student ID Card worth £160 as a Gift - Enrol Now</strong></p><p>Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn.</p><p>Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization.</p><p>The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.</p><p><strong>This Python Bundle Consists of the following Premium courses: </strong></p><ul><li><strong></strong><strong> Course 01</strong>: Python Programming for Everybody</li><li><strong></strong><strong> Course 02</strong>: Data Science with Python</li><li><strong></strong><strong> Course 03</strong>: Machine Learning with Python</li><li><strong></strong><strong> Course 04</strong>: Learn to Use Python for Spatial Analysis in ArcGIS</li><li><strong></strong><strong> Course 05</strong>: Higher Order Functions in Python - Level 03</li><li><strong></strong><strong> Course 06</strong>: Introduction to Data Analysis</li><li><strong></strong><strong> Course 07</strong>: Data Structures Complete Course</li><li><strong></strong><strong> Course 08</strong>: Basic Google Data Studio</li><li><strong></strong><strong> Course 09:</strong> Computer Science: Graph Theory Algorithms</li><li><strong></strong><strong> Course 10</strong>: Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li><strong></strong><strong> Course 11</strong>: Business Intelligence and Data Mining Masterclass</li><li><strong></strong><strong> Course 12</strong>: Cyber Security Incident Handling and Incident Response</li><li><strong></strong><strong> Course 13</strong>: Decision Making and Critical Thinking Decision Making and Critical Thinking</li><li><strong></strong><strong> Course 14</strong>: Time Management</li></ul><p><strong>Benefits you'll get from choosing Apex Learning:</strong></p><ul><li>Pay once and get lifetime access <strong>to 14 CPD courses</strong></li><li>Free e-Learning App for engaging reading materials &amp; helpful assistance</li><li>Certificates, student ID for the Courses included in a one-time fee</li><li>Free up your time - don't waste time and money travelling for classes</li><li>Accessible, informative modules of Courses designed by expert instructors</li><li>Learn about Courses at your ease - anytime, from anywhere</li><li>Study the Courses from your computer, tablet or mobile device</li><li>CPD accredited Courses - improve the chance of gaining professional skills</li></ul><p><strong>Curriculum : </strong></p><p><strong>Course 01: Python Programming for Everybody</strong></p><p>This Python's curriculum has been designed by experts with years of experience behind them. The Python course is extremely dynamic and well-paced to help you understand with ease. You'll discover how to master the skill while exploring relevant and essential topics.</p><p><strong>Module 01</strong></p><ul><li>A Installing Python</li><li>Documentation</li><li>Command Line</li></ul><p>And much more...</p><p><strong>Module 02</strong></p><ul><li>Additional Topics</li><li>If Elif Else</li><li>Iterable</li><li>For</li></ul><p>And much more...</p><p><strong>Module 03</strong></p><ul><li>Number Types</li><li>More Number Types</li><li>Strings</li><li>More Strings</li></ul><p>And much more...</p><p><strong>Module 04</strong></p><ul><li>Comprehensions</li><li>Definitions</li><li>Functions</li><li>Default Arguments</li></ul><p>And much more...</p><p><strong>Module 05</strong></p><ul><li>Function Objects</li><li>Lambda</li><li>Generators</li><li>Closures</li></ul><p>And much more...</p><p><strong>How will I get my Certificate?</strong></p><p>After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement.</p><ul><li><strong>PDF Certificate: Free (For The Title Course)</strong></li><li><strong>Hard Copy Certificate: Free (For The Title Course)</strong></li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
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                                <div>
                                    <div>
                                        140 CPD hours / points
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                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
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                        <div id="who_is_this_course_for">
                            <p>Anyone from any background can enrol in this bundle.</p>
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        <div>Requirements</div>
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                        <div id="requirements">
                            <p>Our course is fully compatible with PCs, Macs, laptops, tablets and Smartphone devices.</p>
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        <div>Career path</div>
    </h2>

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

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

                        </div>

Apex Learning

Python Training Courses

SkillWise

Foundations of Artificial Intelligence

<p><strong>Software Engineering Exclusive Bundle</strong></p><p><strong>*</strong><strong>*19-in-1 Premium Software Engineering Training Bundle**</strong></p><p>Software development is one of the most in-demand occupations, and the UK is an excellent place to begin your career in this industry. To be honest, regardless of your background and experiences, a particular skill set is also essential. So here's how we can assist you.</p><p>We provide a full 19-in-1 Software Engineering bundle course to train you in a range of programming languages, software design, information systems, and databases.</p><p>In this software engineering course, you will learn the fundamentals of software engineering, such as how to analyse algorithms and use arrays and link lists as well. This course will cover several fundamental programming concepts before going on to more advanced topics. You will learn the core concepts of stacks and queues, storing algorithms, trees, heaps and many more.</p><p>This software engineering course is a great place to start if you're looking to start a new career in software engineering/ hone your existing software engineering skills and acquire relevant competencies. This software engineering program is for anyone interested in gaining in-demand skills to help launch a career as a software engineering personnel or improve their business aptitude. All through this self-paced software engineering training, you will engage yourself in the role of a software engineering personeel and acquire the necessary software engineering skills to work with a variety of concepts to gain a competitive advantage in the employment market.</p><p>Along with this Software Engineering course, you will get <strong>19 premium courses, an original </strong><strong>hardcopy</strong>, 19<strong> PDF certificates (Main Course + Additional Courses) </strong>Student ID card as gifts.</p><p><strong>Learning Outcomes of Software Engineering: Coding &amp; Web Development</strong></p><p>By the end of this course, participants will be able to:</p><ul><li>Get an overview of software engineering.</li><li>Learn to analyse algorithms.</li><li>Learn how to work with arrays and linked lists.</li><li>Improve your understanding of stacks and queues.</li><li>Gain a better knowledge of trees and heaps.</li><li>Recognise the importance of sorting algorithms.</li><li>Learn WSDL, Load, Security, REST and Functional Testing in SOAP UI</li><li>Be a competent programmer in python</li><li>Get a solid foundation in JavaScript</li><li>Learn Machine learning with Python</li><li>Code with HTML, CSS, &amp; Javascript</li></ul><p>Enrol now in Software Engineering to advance your career, and use the premium study materials from Apex Learning.</p><p><strong>Software Engineering: Coding &amp; Web Development</strong></p><p>The Software Engineering bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your Software Engineering expertise and essential knowledge, which will assist you in reaching your goal. Moreover, you can learn Software Engineering from any place in your own time without travelling for classes.</p><p><strong>This Software Engineering Bundle Consists of the following Premium courses: </strong></p><ul><li> Course 01: Computer Science With Python</li><li> Course 02: Testing using SOAP UI</li><li> Course 03: Python Programming for Everybody</li><li> Course 04: JavaScript Foundations for Everyone</li><li> Course 05: Machine Learning with Python</li><li> Course 06: Coding with HTML, CSS, &amp; Javascript</li><li> Course 07: Django Rest Framework Level 4</li><li> Course 08: Data Structures Complete Course</li><li> Course 09: Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li> Course 10: Learn Linux in 5 Days</li><li> Course 11: Level 2 Adobe Graphics Design Course</li><li> Course 12: Level 5 CAD Designer Training</li><li> Course 13: Computer Networks Security</li><li> Course 14: jQuery: JavaScript and AJAX Coding Bible</li><li> Course 15: GDPR Data Protection Level 5</li><li> Course 16: Secure Programming of Web Applications</li><li> Course 17: Cyber Security Incident Handling and Incident Response</li><li> Course 18: Computer Hacking Forensic Investigator</li><li> Course 19: Essential IT Skills Level 3</li></ul><p><strong>Certificate:</strong></p><ul><li><strong>PDF Certificate: Free (Previously it was £6*19 = £114)</strong></li><li><strong>Hard Copy Certificate: Free (For The Diploma in Computer Programming Course: Previously it was £10)</strong></li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        205 CPD hours / points
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                                        <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><strong>Software Engineering: Coding &amp; Web Development</strong></p><p>Anyone from any background can enrol in this Software Engineering bundle.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p><strong>Software Engineering: Coding &amp; Web Development</strong></p><p>This Software Engineering course has been designed to be fully compatible with tablets and smartphones.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p><strong>Software Engineering: Coding &amp; Web Development</strong></p><p>The opportunities in the world of software are limitless and rising all the time. The top career paths to pursue with the bundle of Software Engineering courses are given below,</p><ul><li>Software Engineer</li><li>Front-end Software Developer</li><li>Web Developer</li></ul><p>The average salary of these professions ranges from £30,000 to £90,000 per annum in the UK.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Software Engineering: Coding & Web Development - CPD Certified

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

Nexus Human

Data Engineering

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

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

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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