Name: Foundations of Artificial Intelligence: Building Intelligent Systems
Price: 10.99 GBP
Availability: InStock

<strong>A comprehensive package for beginners to learn how to automate social media applications such as WhatsApp, YouTube, and Facebook from scratch using Python. The course will help you understand the impact of social media automation in real-world applications and provide a unique hands-on experience in developing different real-time exciting projects.</strong>
        <p>Welcome to this course on automating social media applications using Python. Let's learn the basic concepts required for automating our routine social media tasks.

We will begin with learning to automate WhatsApp and explore how to automatically send messages to individuals and groups using Python. You will learn to automate sending emails to random users with just a few lines of code. Learn to schedule tasks using Python to implement different social media projects. Explore email automation with Smtplib and link Gmail with SMTP.

We will explore how to fetch and download different stats from YouTube channels as CSV or other file formats. Finally, you will learn to post various types of content on Facebook pages, delete posts, and comment on posts. The course, however, does not cover Python programming lessons separately.

Upon completing the course, you can automate social media platforms such as WhatsApp, YouTube, Facebook, and Gmail. Create automated posts and comments, post and delete content, schedule tasks, and comprehensively advance your social media automation skills.

All resources are available at: https://github.com/PacktPublishing/Social-Media-Automation-using-Python</p>
        <h2>What You Will Learn</h2><p>Automate WhatsApp, install the toolkit, schedule and send messages<br>Link Gmail with SMTP, create email,  attach files  and send<br>Schedule, install, import, and code a simple scheduler<br>Create a YouTube API key and fetch channel and playlist information<br>Automate Facebook, post photos with captions, and comment on a post<br>Understand and implement automation packages and libraries</p>
        <h2>Audience</h2><p>This course is designed for beginners with some programming experience or even those who need to know about data analysis, data manipulation, and more. This course is for individuals wanting to understand and implement automation packages and libraries, master scheduling tasks, automate emails, automate WhatsApp, automate YouTube data, and automate Facebook posts. Basic to intermediate Python programming knowledge is required for the course.</p>
        <h2>Approach</h2><p>The course is crafted to help you understand the concepts with a unique hands-on experience in developing different real-time interesting projects. This straightforward learning-by-doing course will help you in mastering the concepts and methodology. This course is easy to understand, expressive, self-explanatory, and practical with live coding.</p>
        <h2>Key Features</h2><p>Explore the most advanced libraries to automate social media and perform automation tasks accurately * Learn with experimentation to practically implement Facebook, WhatsApp, and email automation * Learn to schedule tasks using Python to implement different social media projects</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Social-Media-Automation-using-Python</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are a group of 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</h4><p></p><br><h4>2. WhatsApp Automation</h4><p></p><br><h4>3. Email Automation</h4><p></p><br><h4>4. Scheduling</h4><p></p><br><h4>5. YouTube Automation</h4><p></p><br><h4>6. Facebook Automation</h4><p></p>

Packt

Social Media

Python

A comprehensive package for beginners to learn how to automate social media applications such as WhatsApp, YouTube, and Facebook from scratch using Python. The course will help you understand the impact of social media automation in real-world applications and provide a unique hands-on experience in developing different real-time exciting projects.

Social Media Automation using Python

General Admission

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        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>The Data Science with Python (30 in 1) CPD Accredited Courses! is perfect for:</p><ul><li>Expand your knowledge and skillset for a fulfilling career with the Data Science with Python bundle.</li><li>Become a more valuable professional by earning CPD certification and mastering in-demand skills with the Data Science with Python bundle.</li><li>Discover your passion or explore new career options with the diverse learning opportunities in the Data Science with Python bundle.</li><li>Learn on your schedule, in the comfort of your home - the Data Science with Python bundle offers ultimate flexibility for busy individuals.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You are warmly invited to register for this bundle. Please be aware that no formal entry requirements or qualifications are necessary. This curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Gain a wide range of skills across various fields, improve your problem-solving capabilities, and keep current with industry trends. Perfect for those aiming for career advancement, exploring a new professional direction, or pursuing personal growth. Begin your journey with the Data Science with Python bundle.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Certificates</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Compliance Central

Data Science with Python (30 in 1) CPD Accredited Courses!

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        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p><strong>This Google Analytics Course - Taking Your Business to the Next Level course is ideal for:</strong></p><ul><li>Students seeking mastery in Level 7 in Strategic Learning &amp; Development</li><li>Professionals seeking to enhance Google Analytics Course - Taking Your Business to the Next Level skills</li><li>Individuals looking for a Level 7 in Strategic Learning &amp; Development-related career.</li><li>Anyone passionate about Level 7 in Strategic Learning &amp; Development</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This <strong>Google Analytics Course - Taking Your Business to the Next Level </strong>doesn't require prior experience and is suitable for diverse learners.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This <strong>Google Analytics Course - Taking Your Business to the Next Level </strong>bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Academy for Health and Fitness

Learning & Development

Online Course Marketing

Google Data Analytics

Google Analytics Individual Qualification

Trainers Course

Google Digital Marketing

Google Analytics Course - Taking Your Business to the Next Level


Earn up to £40K or Get Your Money Back, Guaranteed!!!
Enter a booming industry of Data Analyst as an entry-level professional and potentially earn an average of £30,000-£40,000 annually, with the chance to swiftly reach six figures with experience and expertise. See yourself thriving in this competitive industry and earning a handsome salary, as our Data Analyst Job Ready Program is here to guide you until you secure your desired job.
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Description
The Data Analyst Job Ready Program: 8 Steps to Your Pathway to Success!
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Data Analyst

Data Processing

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        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p><strong>Software Engineering - CPD Certified </strong></p><ul><li>Aspiring Software Engineers</li><li>Coding Enthusiasts</li><li>Web Developers</li><li>Tech-Savvy Individuals</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

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

                        <div id="career_path">
                            <p><strong>Software Engineering - CPD Certified </strong></p><ul><li>Software Developer</li><li>Web Developer</li><li>Machine Learning Engineer</li><li>Python Developer</li><li>Application Developer</li><li>IT Consultant</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD QS Accredited PDF Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p>CPD QS Accredited Hard copy certificate - £29</p></div>

                        </div>

Software Engineering: Coding & Web Development - CPD Certified

<strong>This advanced course created by data analysts who use Alteryx daily while working with their clients teaches data cleansing and manipulation, working in databases, apps, and macros, and breaks down Alteryx's latest product, Alteryx Intelligence Suite, which includes ML tools that introduce individuals to the world of AI</strong>
        <p>Alteryx provides a platform for data preparation, blending, and advanced analytics. The Alteryx platform allows users to access, cleanse, and integrate data from various sources, and perform statistical analysis, predictive modeling, and geospatial analysis. Alteryx offers a range of tools and integrations with other software platforms, including Tableau, Snowflake, and Microsoft Azure for users to easily share data and insights across different teams and applications.

The course begins with an introduction to Alteryx and its features and capabilities. You will also learn about its tools and uses. We will then advance to learning about using multiple files and Calgary, sampling data, and using wild matches and data cleansing, manipulation, binning, and grouping. We will explore how to use RegEx (regular expressions) and parse XML. We will work with In-Database and blend, select, and summarize data. You will learn about macros and using iterative and batch macros and customizing apps and macros. Finally, we will explore machine learning and the Alteryx Intelligence Suite.

Upon completion of the course, you will be able to independently handle data cleansing and manipulation, working in databases, apps, and macros, and acquire skills needed to pass Alteryx's certification as well as on-job skills needed to become data analytic experts.</p>
        <h2>What You Will Learn</h2><p>Learn data cleansing, manipulation, binning, and grouping<br>Understand how to use RegEx (regular expressions) and parse XML<br>Work with In-Database and blend, select, and summarize data<br>Learn about apps and macros, understand app errors and conditions<br>Customize apps and macros and use iterative and batch macros<br>Learn about machine learning and the Alteryx Intelligence Suite</p>
        <h2>Audience</h2><p>This advanced course is designed for individuals with beginner- to intermediate-level knowledge of Alteryx who are looking to build upon those skills. The prerequisites for the course would include an intermediate to advanced understanding of Excel, which would be useful while learning the concepts of the course.</p>
        <h2>Approach</h2><p>This course is designed to be delivered over video-led, high-definition training with concepts being delivered over a classroom-style tutorial and drawing-board experience. The course includes multiple quizzes and exercises to help learn the topics and concepts comprehensively.</p>
        <h2>Key Features</h2><p>Learn about Alteryx on data cleansing, manipulation, working in databases, apps, and macros * Acquire skills needed to pass Alteryx's certification or to become data analytics experts * Explore Alteryx's Intelligence Suite, with ML tools that introduce you to the world of AI</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Alteryx-Advanced</p>
        <h2>About the Author</h2><h3>Simon Sez IT</h3><p>Simon Sez IT has offered technical courses for individuals, small businesses, and Fortune 500 companies since 2008, with thousands of employees who can benefit from the easy-to-learn and hands-on software training. It offers over 8,000 video tutorials on a range of software programs.

Simon Sez IT ensures stress-free eLearning and enhanced employee productivity-whether you implement new software or a technological upgrade in your work environment. With over 600,000 students from 180 countries, Simon Sez IT is the preferred online learning choice for individuals and businesses worldwide.</p>
        <h2>Course Outline</h2><h4>1. Data Cleansing & Manipulation</h4><p></p><br><h4>2. Working in Database</h4><p></p><br><h4>3. Apps and Macros</h4><p></p><br><h4>4. Intelligence Suite</h4><p></p><br><h4>5. Exercises and Course Close</h4><p></p>

Data Visualization

This advanced course created by data analysts who use Alteryx daily while working with their clients teaches data cleansing and manipulation, working in databases, apps, and macros, and breaks down Alteryx's latest product, Alteryx Intelligence Suite, which includes ML tools that introduce individuals to the world of AI

Alteryx Advanced

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><b data-stringify-type="bold" fr-original-style=" color: rgb(209, 210, 211);" style="color: rgb(209, 210, 211); box-sizing: border-box;" id="isPasted"><span fr-original-style="color: rgb(0, 0, 0);" style="color: rgb(0, 0, 0); box-sizing: border-box;">Welcome to Artificial Intelligence Foundations Course. In this course, you&#39;ll learn:<br fr-original-style="" style="box-sizing: border-box;"></span></b></p><ol style="color: rgb(209, 213, 219); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(209, 213, 219);"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Introduction to Artificial Intelligence:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Explore the history, evolution, and various branches of AI.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Understand the ethical implications and societal impact of AI.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Gain insights into the current state and future trends of AI technology.</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Mathematics for AI:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Develop a strong mathematical foundation essential for AI applications.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Cover concepts such as linear algebra, calculus, and probability theory.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Learn how to apply mathematical principles to solve AI problems.</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Knowledge Representation in AI - Part 1:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Examine different methods for representing knowledge in AI systems.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Explore symbolic representation and logical reasoning techniques.</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Knowledge Representation in AI - Part 2:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Dive deeper into knowledge representation techniques.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Explore ontologies, semantic networks, and other advanced topics.</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Machine Learning - Part 1:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Introduce the basics of machine learning algorithms.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Cover supervised and unsupervised learning, and regression.</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Machine Learning - Part 2:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Explore advanced machine learning concepts.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Discuss ensemble methods, dimensionality reduction, and model evaluation.</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Deep Learning:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Understand the fundamentals of neural networks.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Explore deep learning architectures, including convolutional and recurrent neural networks.</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Natural Language Processing:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Study the processing and understanding of human language by machines.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Cover topics such as text analysis, sentiment analysis, and language generation.</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Computer Vision:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Explore the principles and applications of computer vision.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Discuss image recognition, object detection, and image generation.</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Robotics:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Introduce the integration of AI in robotics.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Explore topics such as robot perception, motion planning, and control.</li></ul></li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong style="color: var(--tw-prose-bold); font-weight: 700; box-sizing: border-box;" fr-original-style=" color: var(--tw-prose-bold);">Building AI Applications:</strong></p><ul style="color: initial; box-sizing: border-box;" fr-original-style="color: initial;"><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Learn the practical aspects of developing AI applications.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Discuss real-world case studies and hands-on projects.</li><li style="color: rgb(0, 0, 0); box-sizing: border-box;" fr-original-style="color: rgb(0, 0, 0);">Understand the challenges and considerations in deploying AI solutions.</li></ul></li></ol>

John Academy

Natural Language Processing (NLP)

Robotics

Computer Vision

Unsupervised Machine Learning

Explore the world of Artificial Intelligence with our comprehensive Foundations Course. From understanding the basics of AI and essential mathematical principles to delving into advanced topics like Deep Learning, Natural Language Processing, and Robotics – this course equips you with the knowledge and skills needed to navigate the dynamic landscape of AI. Whether you're a student, professional, or enthusiast, join us on a journey to build a solid foundation in AI and develop practical applications that shape the future. Enroll now and empower yourself to contribute to the exciting field of Artificial Intelligence.

Artificial Intelligence Foundations Course


<p>Feeling Stuck in Your Career? TheWeb Application Penetration Testing - 30 CoursesBundle is Your Skill-Building Solution. This exceptional collection of <strong>30 premium courses</strong> is designed to encourage growth and improve your career opportunities. Suited to meet different interests and goals, the Web Application Penetration Testing bundle provides an engaging learning experience, helping you learn skills across various disciplines.</p><p>With the Web Application Penetration Testing - 30 CoursesBundle, you'll have a personalised journey that aligns with your career goals and interests. This comprehensive package helps you confidently tackle new challenges, whether entering a new field or enhancing your existing knowledge. The Web Application Penetration Testing bundle is your gateway to expanding your career options, increasing job demand, and enhancing your skill set.</p><p>By enrolling in this bundle, you'll receive complimentary PDF certificates for all courses, adding value to your resume at no extra cost. Develop key skills and achieve important progress in your career and personal development. Start your journey today and experience the transformative impact of the Web Application Penetration Testing bundle on your job life and career growth!</p><p><strong>This Web Application Penetration Testing - 30 Courses</strong><strong>Bundle Comprises the Following CPD Accredited Courses:</strong></p><ul><li><strong>Course 01</strong>: Web Application Penetration Testing Course</li><li><strong>Course 02</strong>: Penetration Testing with OWASP ZAP: Mastery course</li><li><strong>Course 03</strong>: CompTIA PenTest+ (Ethical Hacking)</li><li><strong>Course 04</strong>: Secure Programming of Web Applications - Developers and TPMs</li><li><strong>Course 05</strong>: IT Administration and Networking</li><li><strong>Course 06</strong>: CPTE - Certified Penetration Testing Engineer</li><li><strong>Course 07</strong>: Mile2 Certified Network Forensics Examiner (CNFE)</li><li><strong>Course 08</strong>: Computer Networks Security from Scratch to Advanced</li><li><strong>Course 09</strong>: CompTIA Security+ (SY0-601)</li><li><strong>Course 10</strong>: CompTIA CySA+ Cybersecurity Analyst (CS0-002)</li><li><strong>Course 11</strong>: CompTIA Network+ Certification (N10-007)</li><li><strong>Course 12</strong>: Learn Ethical Hacking From A-Z: Beginner To Expert</li><li><strong>Course 13</strong>: The Complete Python 3 Course: Beginner to Advanced!</li><li><strong>Course 14</strong>: Deep Learning &amp; Neural Networks Python - Keras</li><li><strong>Course 15</strong>: Machine Learning with Python Course</li><li><strong>Course 16</strong>: Azure Machine Learning</li><li><strong>Course 17</strong>: JavaScript Basics</li><li><strong>Course 18</strong>: Building Web Applications with Django and PostgreSQL</li><li><strong>Course 19</strong>: Cyber Security Awareness Training</li><li><strong>Course 20</strong>: Cyber Security Incident Handling and Incident Response</li><li><strong>Course 21</strong>: Career Development Plan Fundamentals</li><li><strong>Course 22</strong>: CV Writing and Job Searching</li><li><strong>Course 23</strong>: Learn to Level Up Your Leadership</li><li><strong>Course 24</strong>: Networking Skills for Personal Success</li><li><strong>Course 25</strong>: Ace Your Presentations: Public Speaking Masterclass</li><li><strong>Course 26</strong>: Learn to Make a Fresh Start in Your Life</li><li><strong>Course 27</strong>: Motivation - Motivating Yourself &amp; Others</li><li><strong>Course 28</strong>: Excel: Top 50 Microsoft Excel Formulas in 50 Minutes!</li><li><strong>Course 29</strong>: Decision-Making and Critical Thinking</li><li><strong>Course 30</strong>: Time Management Training - Online Course</li></ul><p><strong>What will make you stand out?</strong></p><p>Upon completion of this online Application Penetration Testing - 30 Courses Bundle, you will gain the following:</p><ul><li>CPD QS Accredited Proficiency with this web Application Penetration Testing Bundle</li><li>After successfully completing the Web Application Penetration Testing bundle, you will receive a FREE PDF Certificate from REED as evidence of your newly acquired abilities.</li><li>Lifetime access to the whole collection of learning materials of this Web Application Penetration Testing Bundle</li><li>The online test with immediate results</li><li>You can study and complete the Web Application Penetration Testing bundle at your own pace.</li><li>Study for the Web Application Penetration Testing bundle using any internet-connected device, such as a computer, tablet, or mobile device.</li></ul><p>The Web Application Penetration Testing - 30 Coursesbundle is a premier learning resource, with each course module holding respected CPD accreditation, symbolising exceptional quality. The content is packed with knowledge and is regularly updated to ensure it remains relevant. This bundle offers not just education but a constantly improving learning experience designed to enrich both your personal and professional development.</p><p>Advance the future of learning with the Web Application Penetration Testing <strong>bundle</strong>, a comprehensive, complete collection of 30 courses. Each course in the Web Application Penetration Testing bundle has been handpicked by our experts to provide a broad range of learning opportunities. Together, these modules form an important and well-rounded learning experience.</p><p>Our mission is to deliver high-quality, accessible education for everyone. Whether you are starting your career, switching industries, or enhancing your professional skills, the Web Application Penetration Testing bundle offers the flexibility and convenience to learn at your own pace. Make the Counselling, Psychology &amp; Art Therapy package your trusted partner in your lifelong learning journey.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>The Web Application Penetration Testing - 30 Courses bundle is perfect for:</p><ul><li>Expand your knowledge and skillset for a fulfilling career with the Web Application Penetration Testing bundle.</li><li>Become a more valuable professional by earning CPD certification and mastering in-demand skills with the Web Application Penetration Testing bundle.</li><li>Discover your passion or explore new career options with the diverse learning opportunities in the Web Application Penetration Testing bundle.</li><li>Learn on your schedule, in the comfort of your home - the Web Application Penetration Testing bundle offers ultimate flexibility for busy individuals.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You are warmly invited to register for this bundle. Please be aware that no formal entry requirements or qualifications are necessary. This curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Gain a wide range of skills across various fields, improve your problem-solving capabilities, and keep current with industry trends. Perfect for those aiming for career advancement, exploring a new professional direction, or pursuing personal growth. Begin your journey with the Web Application Penetration Testing - 30 Courses bundle.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Certificates</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Web Application Penetration Testing - 30 Courses

JavaScript

Web Development

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

ChatGPT and Prompt Engineering With Advanced Data Analysis

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

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

			</div>

Studyhub UK

AI (Artificial Intelligence)

Foundations of Artificial Intelligence: Building Intelligent Systems

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

Nexus Human

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

Microsoft AI-900

AI-900T00 Microsoft Azure AI Fundamentals

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

Microsoft AZ-104

Azure Cognitive Services

AI-102T00 Designing and Implementing an Azure AI Solution

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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					<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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