Name: Data Analytics with Tableau
Price: 39 GBP
Availability: InStock

<p><strong>***24 Hour Limited Time Flash Sale***</strong></p><p><strong> </strong><strong>Google Analytics Course - Taking Your Business to the Next Level </strong><strong>Admission Gifts </strong></p><p><strong> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter</strong></p><p>Are you a professional feeling stuck in your career, struggling to keep up with the ever-changing demands of the industry? Or perhaps you're a beginner, unsure of where to start or how to break into your desired field. Whichever stage you're in, our exclusive Google Analytics Course - Taking Your Business to the Next Level Bundle provides unique insights and tools that can help you achieve your goals.</p><p>Designed to cater to the needs of both seasoned professionals and aspiring newcomers, our Google Analytics Course - Taking Your Business to the Next Level bundle is a comprehensive program that will equip you with the essential skills and knowledge you need to succeed. Whether you're looking to advance in your current role or embark on a new career journey, this bundle has everything you need to take your professional life to the next level.</p><p><strong>But that's not all. When you enrol in Google Analytics Course - Taking Your Business to the Next Level Online Training, you'll receive 25 CPD-Accredited PDF Certificates, Hard Copy Certificates, and our exclusive student ID card, all absolutely free.</strong></p><p><strong>Courses Are Included In this Google Analytics Course - Taking Your Business to the Next Level Career Bundle:</strong></p><ul><li> Course 01: Google Analytics for Everyone</li><li> Course 02: Google Data Studio: Data Analytics</li><li> Course 03: Business Data Analysis</li><li> Course 04: Big Data Analytics with PySpark Tableau Desktop and MongoDB</li><li> Course 05: Big Data Analytics with PySpark Power BI and MongoDB</li><li> Course 06: Introduction to Data Analytics with Tableau</li><li> Course 07: Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau</li><li> Course 08: Develop Big Data Pipelines with R, Sparklyr &amp; Power BI</li><li> Course 09: Quick Data Science Approach from Scratch</li><li> Course 10: Machine Learning with Python Course</li><li> Course 11: Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li> Course 12: Data Science &amp; Machine Learning with Python</li><li> Course 13: SQL for Data Science, Data Analytics and Data Visualization</li><li> Course 14: Customer Analytics Training</li><li> Course 15: Statistical Analysis</li><li> Course 16: Excel Data Analysis</li><li> Course 17: Excel Pivot Tables, Pivot Charts, Slicers, and Timelines</li><li> Course 18: Business Intelligence and Data Mining Diploma</li><li> Course 19: How To Do Keyword Research For SEO &amp; Ranking On Google</li><li> Course 20: Digital Marketing for Business</li><li> Course 21: Digital Marketing Starting an online E-Commerce Store</li><li> Course 22: Marketing Strategies for Business</li><li> Course 23: Strategic Planning and Analysis for Marketing</li><li> Course 24: Ultimate Social Media Marketing course</li><li> Course 25: Bootstrap - Responsive Web Design</li></ul><p>With Level 7 in Strategic Learning &amp; Development, you'll embark on an immersive learning experience that combines interactive lessons with voice-over audio, ensuring that you can learn from anywhere in the world, at your own pace. 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Enrol in Google Analytics Course - Taking Your Business to the Next Level today and take the first step towards achieving your goals and dreams.</p><p><strong>Why buy this Level 7 in Strategic Learning &amp; Development?</strong></p><ul><li><strong>Free CPD Accredited Certificate</strong> upon completion of Level 7 in Strategic Learning &amp; Development</li><li>Get <strong>a free student ID card</strong> with Level 7 in Strategic Learning &amp; Development</li><li><strong>Lifetime access</strong> to the Google Analytics Course - Taking Your Business to the Next Level course materials</li><li>Get instant access to this Google Analytics Course - Taking Your Business to the Next Level course</li><li>Learn Google Analytics Course - Taking Your Business to the Next Level from anywhere in the world</li><li>24/7 tutor support with the Google Analytics Course - Taking Your Business to the Next Level course.</li></ul> <p><strong>Start your learning journey straightaway with our Google Analytics Course - Taking Your Business to the Next Level Training!</strong></p><p>Google Analytics Course - Taking Your Business to the Next Level premium bundle consists of 25 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of the Google Analytics Course - Taking Your Business to the Next Level is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience.</p><p><strong>Certification</strong></p><p>You have to complete the assignment given at the end of the Google Analytics Course - Taking Your Business to the Next Level course. After passing the Google Analytics Course - Taking Your Business to the Next Level exam You will be entitled to claim a PDF &amp; Hardcopy certificate accredited by CPD Quality standards completely free.</p> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <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

General Admission

<p>Unlock a world of opportunities in the dynamic field of software engineering! In the UK, there's an ever-growing demand for skilled software engineers, with a staggering <strong>45,000+ job openings annually</strong>.</p><p>If you aim to enhance your software engineering skills, our comprehensive software engineering course is perfect for you. Designed for success, this software engineering course covers everything from basics to advanced topics in software engineering.</p><p><strong>This Software Engineering &amp; Coding Bundle Contains </strong><strong>6</strong><strong> of Our Premium Courses for One Discounted Price:</strong></p><ul><li><strong> Course 01: </strong>Python Programming for Everybody</li><li><strong> Course 02: </strong>Coding with HTML, CSS, and Javascript</li><li><strong> Course 03: </strong>C# Programming: Beginner to Advanced</li><li><strong> Course 04: </strong>Modern PHP Web Development w/ MySQL, GitHub and Heroku</li><li><strong></strong><strong>Course 05:</strong> Basics of WordPress: Create Unlimited Websites</li><li><strong></strong><strong>Course 06: </strong>Machine Learning with Python Course</li></ul><p>Each lesson in this software engineering course is crafted for easy understanding, enabling you to become proficient in software engineering. Whether you are a beginner or looking to sharpen your existing nursing skills, this software engineering course is the ideal choice.</p><p>Take the first step towards a rewarding career in software engineering now!</p><p><strong>Learning Outcomes: Software Engineering Bundle:</strong></p><ul><li>Master coding with Scratch for creative game development.</li><li>Progress from beginner to advanced C# programming proficiency.</li><li>Dive into machine learning using Python, a key tech skill.</li><li>Build unlimited websites with WordPress, a web development essential.</li><li>Expertise in modern PHP web development with MySQL.</li><li>Harness Python's power for networking, GUI, email, XML, and CGI.</li><li>Acquire a comprehensive coding skill set for versatile applications.</li></ul><p><strong>Why choose our software engineering course?</strong></p><ul><li>FREE Software Engineering certificate accredited</li><li>Get a free student ID card with Software Engineering Training</li><li>Get instant access to this software engineering course.</li><li>Learn Software Engineering from anywhere in the world</li><li>The Software Engineering is affordable and simple to understand</li><li>Software Engineering is an entirely online, interactive lesson with voiceover audio</li><li>Lifetime access to the Software Engineering course materials</li><li>The Software engineering course comes with 24/7 tutor support</li></ul><p>So enroll now in this software engineering course to advance your career!</p> <p><strong>Software Engineering Diploma, CPD-Certified </strong></p><p>Explore the world of Software Engineering with our comprehensive Software Engineering &amp; Coding Bundle, offering six premium courses at an unbeatable price. Dive into Python Programming for Everybody, master HTML, CSS, and Javascript coding, and advance your skills with C# Programming. Unlock the Python Programming Bible, covering Networking, GUI, Email, XML, and CGI. Learn the basics of WordPress for unlimited website creation, and delve into Modern PHP Web Development with MySQL, GitHub, and Heroku. This bundle provides a holistic Software Engineering education, perfect for aspiring engineers and developers, all at an incredible value.</p> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p><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

Apex Learning

Google Data Studio

GDPR

Data Mining

Tableau

Business Intelligence (BI)

Python

ChatGPT

12 in 1 Career Guided Programme | Python, Data Science, ML, Tableau, SQL Programming, Data Mining, Business Analysis | 130 CPD Points | Tutor Support | Lifetime Access

Data Analyst (Data Analytics) Diploma - 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>

Packt

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

Data Science

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>

Compliance Central

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

			</div>

Studyhub UK

AI (Artificial Intelligence)

Foundations of Artificial Intelligence: Building Intelligent Systems

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

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

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

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

Bitcoin

Programming

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

Financial Analysis - Build a ChatGPT Pairs Trading Bot

<p>Tired of browsing and searching for a <strong>Data Analysis and Data Science </strong> course you are looking for? Can't find the complete package that fulfils all your needs? Then don't worry as you have just found the solution. Take a minute and look through this extensive bundle that has everything you need to succeed.</p> <p>After surveying thousands of learners just like you and considering their valuable feedback, this all-in<strong>-</strong>one Data Analysis and Data Science bundle has been designed by industry experts. We prioritised what learners were looking for in a complete package and developed this in-demand Data Analysis and Data Science course that will enhance your skills and prepare you for the competitive job market.</p> <p>Also, our experts are available for answering your queries on Data Analysis and Data Science and help you along your learning journey. Advanced audio-visual learning modules of these Data Analysis and Data Science courses are broken down into little chunks so that you can learn at your own pace without being overwhelmed by too much material at once.</p> <p>Furthermore, to help you showcase your expertise in Data Analysis and Data Science, we have prepared a special gift of 1 hardcopy certificate and 1 PDF certificate for the title course completely free of cost. These certificates will enhance your credibility and encourage possible employers to pick you over the rest.</p> <p><strong>This Data Analysis and Data Science Bundle Consists of the following Premium courses: </strong></p> <ul> <li><strong> </strong><strong>Course 01: </strong>Introduction to Data Analysis</li><li><strong> </strong><strong>Course 02: </strong>Python for Data Analysis</li><li><strong> </strong><strong>Course 03: </strong>Statistical Analysis</li><li><strong> </strong><strong>Course 04: </strong>SQL NoSQL Big Data and Hadoop</li><li><strong> </strong><strong>Course 05: </strong>Complete Microsoft Power BI 2021</li><li><strong> </strong><strong>Course 06: </strong>Data Analysis in Excel Level 3 Course</li><li><strong> </strong><strong>Course 07: </strong>Data Analytics with Tableau</li><li><strong> </strong><strong>Course 08: </strong>Basic Google Data Studio</li><li><strong> </strong><strong>Course 09: </strong>Business Analytics</li><li><strong> </strong><strong>Course 10: </strong>Complete Introduction to Business Data Analysis Level 3</li><li><strong> </strong><strong>Course 11: </strong>Business Intelligence and Data Mining Masterclass</li><li><strong> </strong><strong>Course 12: </strong>Research Methods in Business</li><li><strong> </strong><strong>Course 13: </strong>Computer Science: Graph Theory Algorithms</li><li><strong> </strong><strong>Course 14: </strong>Data Protection and Data Security Level 2</li></ul> <p>Enrol now in Data Analysis and Data Science to advance your career, and use the premium study materials from Apex Learning.</p> <p><strong>How will I get my Certificate?</strong></p> <p>After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement.</p> <ul> <li><strong>PDF Certificate: Free (For The Title Course)</strong></li><li><strong>Hard Copy Certificate: Free (For The Title Course)</strong></li></ul><p>The bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your Data Analysis and Data Science expertise and essential knowledge, which will assist you in reaching your goal.</p> <p><strong>Curriculum of Bundle</strong></p> <p><strong> Course 01: Introduction to Data Analysis</strong></p> <ul> <li>Module 01: Introduction</li><li>Module 02: Agenda and Principles of Process Management</li><li>Module 03: The Voice of the Process</li><li>Module 04: Working as One Team for Improvement</li><li>Module 05: Exercise: The Voice of the Customer</li><li>Module 06: Tools for Data Analysis</li><li>Module 07: The Pareto Chart</li><li>Module 08: The Histogram</li><li>Module 09: The Run Chart</li><li>Module 10: Exercise: Presenting Performance Data</li><li>Module 11: Understanding Variation</li><li>Module 12: The Control Chart</li><li>Module 13: Control Chart Example</li><li>Module 14: Control Chart Special Cases</li><li>Module 15: Interpreting the Control Chart</li><li>Module 16: Control Chart Exercise</li><li>Module 17: Strategies to Deal with Variation</li><li>Module 18: Using Data to Drive Improvement</li><li>Module 19: A Structure for Performance Measurement</li><li>Module 20: Data Analysis Exercise</li><li>Module 21: Course Project</li><li>Module 22: Test your Understanding</li></ul> <p><strong> Course 02: Python for Data Analysis</strong></p> <ul> <li>Welcome, Course Introduction &amp; overview, and Environment set-up</li><li>Python Essentials</li><li>Python for Data Analysis using NumPy</li><li>Python for Data Analysis using Pandas</li><li>Python for Data Visualization using matplotlib</li><li>Python for Data Visualization using Seaborn</li><li>Python for Data Visualization using pandas</li><li>Python for interactive &amp; geographical plotting using Plotly and Cufflinks</li><li>Capstone Project - Python for Data Analysis &amp; Visualization</li><li>Python for Machine Learning (ML) - scikit-learn - Linear Regression Model</li><li>Python for Machine Learning - scikit-learn - Logistic Regression Model</li><li>Python for Machine Learning - scikit-learn - K Nearest Neighbors</li><li>Python for Machine Learning - scikit-learn - Decision Tree and Random Forests</li><li>Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs)</li><li>Python for Machine Learning - scikit-learn - K Means Clustering</li><li>Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA)</li><li>Recommender Systems with Python - (Additional Topic)</li><li>Python for Natural Language Processing (NLP) - NLTK - (Additional Topic)</li></ul> <p><strong> Course 03: Statistical Analysis</strong></p> <ul> <li>Module 01: The Realm of Statistics</li><li>Module 02: Basic Statistical Terms</li><li>Module 03: The Center of the Data</li><li>Module 04: Data Variability</li><li>Module 05: Binomial and Normal Distributions</li><li>Module 06: Introduction to Probability</li><li>Module 07: Estimates and Intervals</li><li>Module 08: Hypothesis Testing</li><li>Module 09: Regression Analysis</li><li>Module 10: Algorithms, Analytics and Predictions</li><li>Module 11: Learning From Experience: The Bayesian Way</li><li>Module 12: Doing Statistics: The Wrong Way</li><li>Module 13: How We Can Do Statistics Better</li></ul> <p><strong> Course 04: SQL NoSQL Big Data and Hadoop</strong></p> <ul> <li>Module 01: Introduction</li><li>Module 02: Relational Database Systems</li><li>Module 03: Database Classification</li><li>Module 04: Key-Value Store</li><li>Module 05: Document-Oriented Databases</li><li>Module 06: Search Engines</li><li>Module 07: Wide Column Store</li><li>Module 08: Time Series Databases</li><li>Module 09: Graph Databases</li><li>Module 10: Hadoop Platform</li><li>Module 11: Big Data SQL Engines</li><li>Module 12: Distributed Commit Log</li><li>Module 13: Summary</li></ul> <p><strong> Course 05: Complete Microsoft Power BI 2021</strong></p> <ul> <li>Module 01: Introduction</li><li>Module 02: Preparing our Project</li><li>Module 03: Data Transformation - The Query Editor</li><li>Module 04: Data Transformation - Advanced</li><li>Module 05: Creating a Data Model</li><li>Module 06: Data Visualization</li><li>Module 07: Power BI &amp; Python</li><li>Module 08: Storytelling with Data</li><li>Module 09: DAX - The Essentials</li><li>Module 10: DAX - The CALCULATE function</li><li>Module 11: Power BI Service - Power BI Cloud</li><li>Module 12: Row-Level Security</li><li>Module 13: More data sources</li><li>Module 14: Next steps to improve &amp; stay up to date</li></ul> <p><strong> Course 06: Data Analysis in Excel Level 3 Course</strong></p> <ul> <li>Modifying a Worksheet</li><li>Working with Lists</li><li>Analyzing Data</li><li>Visualizing Data with Charts</li><li>Using PivotTables and PivotCharts</li><li>Working with Multiple Worksheets and Workbooks</li><li>Using Lookup Functions and Formula Auditing</li><li>Automating Workbook Functionality</li><li>Creating Sparklines and Mapping Data</li><li>Forecasting Data</li></ul> <p><strong> Course 07: Data Analytics with Tableau</strong></p> <ul> <li>Module 01: Introduction to the Course</li><li>Module 02: Project 1: Discount Mart (Sales and Profit Analytics)</li><li>Module 03: Project 2: Green Destinations (HR Analytics)</li><li>Module 04: Project 3: Superstore (Sales Agent Tracker)</li><li>Module 05: Northwind Trade (Shipping Analytics)</li><li>Module 06: Project 5: Tesla (Stock Price Analytics)</li><li>Module 07: Bonus: Introduction to Database Concepts</li><li>Module 08: Tableau Stories</li></ul> <p><strong> Course 08: Basic Google Data Studio</strong></p> <ul> <li>Module 01: Introduction to GDS</li><li>Module 02: Data Visualization</li><li>Module 03: Geo-visualization</li><li>Module 04: A Socio-Economic Case Study</li></ul> <p><strong> Course 09: Business Analytics</strong></p> <ul> <li>Module 01: What is business analysis?</li><li>Module 02: Strategy analysis</li><li>Module 03: Collaboration</li><li>Module 04: Requirements analysis and Design definition</li><li>Module 05: Requirements lifecycle management</li><li>Module 06: Solution quality</li><li>Module 07: Stakeholder management</li><li>Module 08: BA Governance</li><li>Module 09: Legal notes and Copyright information</li></ul> <p><strong> Course 10: Complete Introduction to Business Data Analysis Level 3</strong></p> <ul> <li>Module 1: Statistics Fundamentals</li><li>Module 2: Data Analysis</li><li>Module 3: Probability</li><li>Module 4: Random Variables and Discrete Distributions</li><li>Module 5: Continuous Distributions</li><li>Module 6: Sampling Distributions</li><li>Module 7: Confidence Interval</li><li>Module 8: Hypothesis Testing with One Sample</li><li>Module 9: Hypothesis Testing with Two Samples</li><li>Module 10: The Chi-Square Distribution</li><li>Module 11: F Distribution and One-Way ANOVA</li><li>Module 12: Correlation analysis</li><li>Module 13: Simple Linear Regression Analysis</li></ul> <p><strong> Course 11: Business Intelligence and Data Mining Masterclass</strong></p> <ul> <li>Module 01: What is Business Intelligence?</li><li>Module 02: Starting Case in understanding BI needs in diff phase of business</li><li>Module 03: Decision Making Process and Need of IT systems</li><li>Module 04: Problem Structure and Decision Support System</li><li>Module 05: Introduction to BI Applications</li><li>Module 06: Dashboard presentation systems</li><li>Module 07: Different Types of Charts used in 131 Dashboards</li><li>Module 08: Good Dashboard and BSC</li><li>Module 09: Examples of Bad Dashboards 1</li><li>Module 10: Examples of Bad Dashboards 2</li></ul> <p>And much more...</p> <p><strong> Course 12: Research Methods in Business</strong></p> <ul> <li>Section 01: Applied Project &amp; Research Methods in Business</li><li>Section 02: Writing a Purpose / Quantitative and Qualitative Research Approaches</li><li>Section 03: Mixed Method Research Approaches, Ethical Considerations &amp; Writing</li><li>Effectively Written Methodology Part 3 !@@</li><li>Section 04: Writing Data Collection Tools, Qualitative &amp; Quantitative Data Analysis</li><li>Section 05: Comparing Findings to Literature and Writing the Final Paper</li></ul> <p><strong> Course 13: Computer Science: Graph Theory Algorithms</strong></p> <ul> <li>Module 00: Promo</li><li>Module 01: Introduction</li><li>Module 02: Common Problem</li><li>Module 03: Depth First Search</li><li>Module 04: Breadth First Search</li><li>Module 05: Breadth First Search Shortest Path on a Grid</li></ul> <p>And much more...</p> <p><strong> Course 14: Data Protection and Data Security Level 2</strong></p> <ul> <li>GDPR Basics</li><li>GDPR Explained</li><li>Lawful Basis for Preparation</li><li>Rights and Breaches</li><li>Responsibilities and Obligations</li></ul><h2>
        
        <div>CPD</div>
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                                        165 CPD hours / points
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                                            Accredited by CPD Quality Standards
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        <div>Who is this course for?</div>
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                             <p>Anyone from any background can enrol in this Data Analysis and Data Science bundle.</p> 
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        <div>Requirements</div>
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                             <p>Our Data Analysis and Data Science course is fully compatible with PCs, Macs, laptops, tablets and Smartphone devices.</p> 
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        <div>Career path</div>
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                             <p>Having this Data Analysis and Data Science expertise will increase the value of your CV and open you up to multiple job sectors.</p> 
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        <div>Certificates</div>
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                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            Included
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            <div> <p>You will get the PDF Certificate for the title course (<strong>Introduction to Data Analysis</strong>) absolutely Free! </p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
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            <div> <p>You will get the Hard Copy certificate for the title course (<strong>Introduction to Data Analysis</strong>) absolutely Free! Other Hard Copy certificates are available for £10 each. </p> <p>Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.</p> </div>

                        </div>

Data Analytics with Tableau

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

Nexus Human

Data Engineering

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

Azure Machine Learning

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

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

Machine Learning (ML)

Machine Learning for Apps Level 4

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

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

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

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

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

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

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

Python Machine Learning Crash Course for Beginners

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

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

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

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

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

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

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

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

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

Machine Learning for Absolute Beginners - Level 2

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

TensorFlow

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

The Complete Machine Learning Course with Python

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

Machine Learning - Use in Flutter

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

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

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

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

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

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

Deep Learning

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

632 Courses delivered Online

Google Analytics Course - Taking Your Business to the Next Level

By Academy for Health and Fitness

Software Engineering: Coding & Web Development - CPD Certified

By Academy for Health and Fitness

Data Analyst (Data Analytics) Diploma - CPD Certified

By Apex Learning

Alteryx Advanced

By Packt

Artificial Intelligence Foundations Course

By John Academy

Web Application Penetration Testing - 30 Courses

By Compliance Central

ChatGPT and Prompt Engineering With Advanced Data Analysis

By Packt

Foundations of Artificial Intelligence: Building Intelligent Systems

By Studyhub UK

Financial Analysis - Build a ChatGPT Pairs Trading Bot

By Packt

Data Analytics with Tableau

By Apex Learning