Name: Statistics and Mathematics for Data Science and Data Analytics
Price: 82.99 GBP
Availability: InStock

<p>Tired of searching and accumulating all the relevant courses for this specific field? It takes a substantial amount of your time and, more importantly, costs you a fortune! Well, we have just come up with the ultimate solution for you by giving this all-inclusive Google Analytics mega bundle.</p><p>This 40 courses mega bundle keeps you up-to-date in this field and takes you one step ahead of others. Keeping in mind the latest advancements in this ever-emerging sector, the Google Analytics bundle covers all the state-of-the-art tools and techniques required to become a competent worker in this area.</p><p>You will encounter specific courses relevant to the sector. We take you from the most introductory fundamentals to advance knowledge in a step-by-step manner. In addition, the Google Analytics bundle also consists of courses related to some soft skills that you will need to succeed in any industry or job sector.</p><p><strong>This </strong><strong>Google Analytics </strong><strong>Bundle consists of the following premium courses:</strong></p><ul><li>Course 01: Google Data Studio: Data Analytics</li><li>Course 02: Diploma in Data Analysis Fundamentals</li><li>Course 03: Learn How To Optimize Your Google My Business Page In 2016</li><li>Course 04: How To Do Keyword Research For SEO &amp; Ranking On Google</li><li>Course 05: Excel Pivot Tables for Data Reporting</li><li>Course 06: Research Methods in Business</li><li>Course 07: Business Intelligence and Data Mining</li><li>Course 08: Microsoft Power BI - Master Power BI in 90 Minutes!</li><li>Course 09: PowerBI Formulas</li><li>Course 10: Digital Marketing for Beginners to Advanced</li><li>Course 11: Digital Marketing Starting an online E-Commerce Store</li><li>Course 12: Marketing Strategies for Business</li><li>Course 13: Strategic Planning and Analysis for Marketing</li><li>Course 14: Statistical Analysis</li><li>Course 15: Ultimate Social Media Marketing course</li><li>Course 16: Python Course</li><li>Course 17: Complete Online Marketing &amp; Advertising Course</li><li>Course 18: Social Media Marketing-The Step by Step Guide</li><li>Course 19: Create Your First Responsive Website</li><li>Course 20: Data Science &amp; Machine Learning with Python</li><li>Course 21: Fundamentals of Business Analysis</li><li>Course 22: Excel Data Analysis</li><li>Course 23: Quick Data Science Approach from Scratch</li><li>Course 24: Basics of WordPress: Create Unlimited Websites</li><li>Course 25: The Ultimate SEO Blueprint - Easily Ranking #1 On Google</li><li>Course 26: Complete Communication Skills Master Class for Life</li><li>Course 27: Public Speaking</li><li>Course 28: Minute Taking Executive Training</li><li>Course 29: Receptionist Skills</li><li>Course 30: Time Management Training - Online Course</li></ul><p><strong>Moreover, this bundles include 10 career-focused courses:</strong></p><ul><li>Course 01: Career Development Plan Fundamentals</li><li>Course 02: CV Writing and Job Searching</li><li>Course 03: Interview Skills: Ace the Interview</li><li>Course 04: Video Job Interview for Job Seekers</li><li>Course 05: Create a Professional LinkedIn Profile</li><li>Course 06: Business English Perfection Course</li><li>Course 07: Networking Skills for Personal Success</li><li>Course 08: Boost Your Confidence and Self-Esteem</li><li>Course 09: Public Speaking Training</li><li>Course 10: Learn to Fight Procrastination</li></ul><p>Our cutting-edge learning package offers top-notch digital aid and first-rate tutor support. You will acquire the crucial hard and soft skills needed for career advancement because this bundle has been thoroughly examined and is career-friendly. So don't overthink! Enrol today.</p><p><strong>Learning Outcomes</strong></p><p>This unique Google Analytics mega bundle will help you to-</p><ul><li>Quench your thirst for knowledge</li><li>Be up-to-date about the latest advancements</li><li>Achieve your dream career goal in this sector</li><li>Know the applicable rules and regulations needed for a professional in this area</li><li>Acquire some valuable knowledge related to Google Analytics to uplift your morale</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 expertise and essential knowledge, which will assist you in reaching your goal. Moreover, you can learn from any place in your own time without travelling for classes.</p><p><strong>Certificate:</strong></p><ul><li><strong>PDF Certificate: Free for all </strong><strong>40</strong><strong> courses</strong></li><li><strong>Hard Copy Certificate: Free (For The Title Course: Previously it was £10)</strong></li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        400 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 Google Analytics bundle is designed to assist anyone with a curious mind, anyone looking to boost their CVs or individuals looking to upgrade their career to the next level can also benefit from the learning materials.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>The courses in this bundle has been designed to be fully compatible with tablets and smartphones.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This Google Analytics bundle will give you an edge over other competitors and will open the doors for you to a plethora of career opportunities.</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Apex Learning

Google Analytics for Everyone - Zero to Hero

General Admission

<p>Unlock Your Potential with the Ultimate Software Developer Training Bundle!</p><p>Are you looking to boost your career in the tech industry? Do you want to expand your skillset and become a well-rounded software developer? Then look no further than our 20-in-1 Ultimate Software Developer bundle!</p><p>This comprehensive bundle includes 25 courses that cover everything from IT support and network security to ethical hacking and web development. You'll learn the latest technologies and industry-standard tools, all taught by expert instructors with real-world experience.</p><p>But that's not all - we also offer 5 career-guided courses to help you land your dream job. You'll learn how to create a professional LinkedIn profile, write a winning CV, ace your job interviews, and more.</p><p>Whether you're a beginner or an experienced professional, this bundle is perfect for anyone who wants to take their software development career to the next level. And with lifetime access to all the courses, you can learn at your own pace and revisit the material whenever you need to.</p><p>Don't miss out on this incredible opportunity to supercharge your career. Enrol in the Ultimate Software Developer Training bundle today and unlock your full potential!</p><p><strong>This 20-in 1 Ultimate Software Developer bundle consists of the following Courses:</strong></p><ul><li>Course 01: IT Support Technician</li><li>Course 02: Network Security and Risk Management</li><li>Course 03: CompTIA Network</li><li>Course 04: Information Management</li><li>Course 05: Web Application Penetration Testing Course</li><li>Course 06: CompTIA Cloud+ (CV0-002)</li><li>Course 07: Learn Ethical Hacking with Kali Linux</li><li>Course 08: Computer Operating System and Troubleshooting</li><li>Course 09: Building Your Own Computer Course</li><li>Course 10: It: Ethical Hacking, IT Security and IT Cyber Attacking</li><li>Course 11: SQL Database Administrator</li><li>Course 12: MS Word Essentials - The Complete Word Course - Level 3</li><li>Course 13: Data Protection (GDPR) Practitioner</li><li>Course 14: Coding with Scratch</li><li>Course 15: Diploma in Front-End Web Development (HTML, CSS, &amp; Bootstrap)</li><li>Course 16: Data Science and Visualisation with Machine Learning</li><li>Course 17: SQL For Data Analytics &amp; Database Development</li><li>Course 18: PLC Programming Diploma</li><li>Course 19: Cisco ASA Firewall Training Course</li><li>Course 20: Create Your First Responsive Website</li></ul><p><strong>Additionally, you will get 5 other career-guided courses in this Software Developer bundle:</strong></p><ul><li>Course 01: Career Development Plan Fundamentals</li><li>Course 02: CV Writing and Job Searching</li><li>Course 03: Interview Skills: Ace the Interview</li><li>Course 04: Video Job Interview for Job Seekers</li><li>Course 05: Create a Professional LinkedIn Profile</li></ul><p><strong><em>So, stop scrolling down and procure the skills and aptitude with Apex Learning to outshine all your peers by enrolling in this </em></strong><strong>Software Developer<em> bundle</em>.</strong></p><p><strong>Learning Outcomes of Software Developer bundle </strong></p><p>This career-oriented bundle will help you to</p><ul><li>Gain the ability to excel in the role of Software Developer</li><li>Know your responsibilities as Software Developer and convey your experience to others</li><li>Gain excellent interpersonal and communication skills</li><li>Know the pros and cons of working as Software Developer</li><li>Adhere to the regulations around this area</li><li>Gain time and risk management skills to ensure efficiency</li><li>Manipulate technological advancement to become more effective</li><li>Maintain moral standards and set an example for your peers</li></ul><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>Anyone from any background can enrol in this Software Developer bundle.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Software Developer course has been designed to be fully compatible with tablets and smartphones.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

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

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

                        </div>

Academy for Health and Fitness

Online Course Marketing

Certified in Cybersecurity (CC)

Certificate in Information Security Management Principles (CISMP)

Cyber Security

Cyber Security Foundation

Certified Cyber Professional (CCP)

Cyber Security Programming MasterClass- CPD Certified

<p>Explore your <strong>AWS cloud</strong> knowledge with our continuously updated Level 7 Diploma in AWS Cloud Practitioner library. Delve into advanced AWS topics such as serverless computing and machine learning. Learn from industry professionals who bring their experience to cloud concepts and technologies. Engaging instruction and practical application will help you pick up new skills more quickly. Enrol to learn about cloud computing, advance to a higher-paying job, and save money without compromising value.</p><p>This premium QLS-endorsed AWS Cloud Practitioner course comes with intensive study modules, dedicated tutor assistance, customer support, flexible learning opportunities and a globally recognised certification. Thus, completing our AWS Cloud Practitioner course will give you limitless possibilities anywhere and everywhere, since the value of QLS-endorsed courses is relatively high!!</p><p>What's more, the quality of our first-class course is uncompromising for both you and us. You will be taught the richest materials from this exquisite AWS Cloud Practitioner course, enabling you to open up a vast world of opportunities to grasp and gain success. For the price point, this course is an absolute steal! It's an investment you do not want to miss out on!</p><p>So, why delay any longer? Benefit your career progression by joining us today and distinguish yourself as unquestionably the most skilled in your workplace!</p><p><strong>Why Learn with us: </strong></p><p>As one of the leading e-learning service providers globally, we strive to provide all our learners with the best e-learning experience possible that can make a real difference in their career progression.</p><p><strong>Our Service for the </strong><strong>AWS Cloud Practitioner</strong><strong> Includes:</strong></p><ul><li>Intensive Study Notes</li><li>Tutor Support</li><li>Customer Support</li><li>Assignment Help</li><li>QLS Endorsed Certificate</li><li>24/7 Learning Portal Access</li><li>Widely Compatible Study Materials</li></ul><p><strong>Course Curriculum of </strong><strong>AWS Cloud Practitioner</strong></p><ul><li>Introduction to Cloud Computing</li><li>First Steps into Amazon Web Services</li><li>Identity and Access Management (IAM)</li><li>Networking AWS-Virtual Private Clouds</li><li>Elastic Compute Cloud (EC2)</li><li>Amazon Storage</li><li>Fault Tolerance and Elasticity</li><li>DNS and Content Delivery Networks</li><li>Monitoring, Auditing and Alerts</li><li>Databases</li><li>Serverless Computing</li><li>Security and Compliance</li><li>AWS Billing and Support Services</li></ul><p><strong>Assessment &amp; Certification</strong></p><p>Learners must submit a comprehensive summarised assignment on all the units that our expert tutor will assess if all the learning outcomes are met as per the standard set by QLS. After the quality check, the learner will receive the AWS Cloud Practitioner certificate.</p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme.</strong></p><p>An endorsed certificate will be issued to learners at the course's end to recognise completion. Provided that the learner completes all the assessments of a system, he can claim a certificate.</p><p><strong>Endorsement:</strong></p><p>QLS endorsed this certified course as a highly qualified, non-regulated provision and training programme. There will be a trainer to answer all your questions, including your progression routes into further higher education.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>Our course is open to everyone. This <strong>Level 7 Diploma in AWS Cloud Practitioner </strong>is ideal for those who aspire to gain in-depth knowledge &amp; efficient skills to embark into the relevant sector as a promising potential.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal requirements needed for this <strong>Level 7 Diploma in AWS Cloud Practitioner </strong>course. So, learners do not require prior qualifications to enrol in this course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p><strong>Level 7 Diploma in AWS Cloud Practitioner</strong> opens opportunities in the following job roles:</p><ul><li>Principal Cloud Solutions Architect (£80,000 - £105,000)</li><li>AWS Cloud DevOps Engineer (£70,000 - £85,000)</li><li>Cybersecurity Analyst - London - Banking - Cloud Platforms (£50,000 - £55,000)</li><li>Software Developer Go, PHP, AWS (£40,000 - £40,001)</li><li>Salesforce Marketing Cloud Consultant (£50,000 - £75,000)</li></ul>
                        </div>

Kingston Open College

Psychological Wellbeing Practitioner

Clinical Practitioner

AWS Certified Developer

AWS Certified Cloud Practitioner

NLP Practitioner

AWS Certified Solutions Architect

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

Level 7 Diploma in AWS Cloud Practitioner - QLS Endorsed

<strong>If you are searching for the right course to learn about iOS Development from scratch, you've just found it.

This course will take you from a complete beginner to a confident practitioner, as we focus on practical iOS development without neglecting the theory.</strong>
        <p>Do you know why application development with Swift is gaining a huge popularity?

The reason being programming in Swift will bring down the cost of software development, the speed of implementation, and the possibility of further innovation.

In this course, you are going to write real-life example apps, games, clones, and much more. You will learn and practice all details of Swift 5 and iOS development during these projects and challenges and will also get introduced to SwiftUI.

The topics that will be covered are Swift 5 beginner to advanced techniques, layout, push notifications, Parse, Firebase, Cocoapods, OneSignal, maps, core data, JSON and API, Back4App servers, social media development, ML, augmented reality, games with Sprite Kit, SwiftUI, design essentials with Adobe XD, widgets, and app storage.

You are also going to work on projects such as Art Book, Catch the Kenny, Currency Converter, Travel Book, Foursquare Clone, Instagram Clone, Snapchat Clone, Angry Bird Clone, Image Recognition App, and so much more. We are going to cover both Firebase and Parse with their latest and most updated versions!

By the end of the course, you will be able to write and design a real app and upload it to the AppStore at the end of this course. When you finish this course, you will be able to understand how you can write any iOS app you want.

All the code files and resource files are placed on the GitHub repository at https://github.com/PacktPublishing/The-Complete-iOS-14-iOS-13-Developer-Course---and-SwiftUI-</p>
        <h2>What You Will Learn</h2><p>iOS 14/iOS 13 development with Swift 5 and Xcode 12<br>Master Swift 5 programming skills<br>Build an iPhone/iPad app and put them to the AppStore<br>Cover both Firebase and Parse with their latest and most updated versions<br>Write real-life example apps, games, and clones<br>Crash course on SwiftUI</p>
        <h2>Audience</h2><p>This course is best suited for the individual who wants to learn about coding and preferable to iOS development. The one who wants to scale up their iOS development skills and master Swift skills will also benefit.<br><br>You need not have any prior experience or knowledge in coding; just be ready with your MacBook and stable internet connection.</p>
        <h2>Approach</h2><p>This is a comprehensive and hands-on course to learn iOS 14 and Swift 5 coding. You do not have to have any prior experience in order to take this course. This course is going to start from scratch and make our way to the top.</p>
        <h2>Key Features</h2><p>One year web hosting from Eco Webhosting * Get 300$ worth of visuals to use in your app design * Pass and get CCA Certification participation for Swift Level 1 and Swift Level 2 for free</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/The-Complete-iOS-14-iOS-13-Developer-Course---and-SwiftUI-</p>
        <h2>About the Author</h2><h3>Codestars By Rob Percival</h3><p>Rob Percival is a highly regarded web developer and Udemy instructor with over 1.7 million students. Over 500,000 of them have taken Rob's Complete Web Developer Course 2.0, as well as his Android Developer and iOS Developer courses.</p><br><h3>Atil Samancioglu</h3><p>Atil Samancioglu is a best-selling online instructor and has instructed more than 80,000 students on Ethical Hacking. Atil has trained more than 20.000 students on the following Ethical Hacking subjects: Kali Linux, Metasploit, metasploitable, social engineering, Nmap, Man-in-the-Middle attacks, MITM proxy, Beef, Wireshark, Maltego, SQL Injection, XSS, social media safety, VPN server creation, Meterpreter, Ettercap, WPA Enterprise. He currently works as eBusiness Team Leader in a multinational company.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. XCode Overview & Getting Started</h4><p></p><br><h4>3. Swift 5 Deep Dive</h4><p></p><br><h4>4. Layout</h4><p></p><br><h4>5. Advanced iOS Features: Permanent Storage, Segue, Alert Message, Timers, Gestures</h4><p></p><br><h4>6. First Game: Catch the Kenny!</h4><p></p><br><h4>7. TableView: Landmark Book</h4><p></p><br><h4>8. Intermediate Swift: OOP</h4><p></p><br><h4>9. Core Data: Art Book</h4><p></p><br><h4>10. Maps: Travel Book</h4><p></p><br><h4>11. iOS Special Features</h4><p></p><br><h4>12. API & JSON: Currency Converter</h4><p></p><br><h4>13. Firebase: Instagram Clone</h4><p></p><br><h4>14. Push Notifications</h4><p></p><br><h4>15. Parse: Foursquare Clone</h4><p></p><br><h4>16. Sprite Kit: Angry Birds Clone</h4><p></p><br><h4>17. Advanced Swift</h4><p></p><br><h4>18. Crash Course on SwiftUI</h4><p></p><br><h4>19. Firebase Mastery: Snapchat Clone</h4><p></p><br><h4>20. Machine Learning: MLKit</h4><p></p><br><h4>21. Augmented Reality</h4><p></p><br><h4>22. How to Make Money From Your App?</h4><p></p><br><h4>23. Uploading Your App to AppStore</h4><p></p><br><h4>24. How to Make a Website for Your App</h4><p></p><br><h4>25. Introduction to Design</h4><p></p><br><h4>26. Design Practice</h4><p></p><br><h4>27. WidgetKit: iOS 14 Only</h4><p></p>

Packt

Swift

Mobile

iOS Development

Xcode

If you are searching for the right course to learn about iOS Development from scratch, you've just found it.

This course will take you from a complete beginner to a confident practitioner, as we focus on practical iOS development without neglecting the theory.

The Complete iOS 14 / iOS 13 Developer Course - and SwiftUI

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>This course is intended for software testers, architects, engineers, or other related roles, who wish to apply AI to software testing practices within their enterprise.<br />While there are no specific pre-requisites for this course, it would be helpful is the attendee has familiarity with basic scripting (Python preferred) and be comfortable with working from the command line (for courses that add the optional hands-on labs). Attendees without basic scripting skills can follow along with the hands-on labs or demos.</p>Overview<p>This course introduces AI and related technologies from a practical applied software testing perspective. Through engaging lecture and demonstrations presented by our expert facilitator, students will explore:<br />Exploring AI<br />Introduction to Machine Learning<br />Introduction to Deep Learning<br />Introduction to Data Science<br />Artificial Intelligence (AI) in Software Testing<br />Implementing AI in Test Automation<br />Innovative AI Test Automation Tools for the Future</p><p>Implementing AI in Software Testing / AI in Test Automation is an introductory-level course for attendees new to AI, Machine Learning or Deep Learning who wish to automate software testing tasks leveraging AI. The course explores the essentials of AI, ML and DL and how the integrate into IT business operations and initiatives. Then the course moves to specifics about the skills, techniques and tools used to apply AI to common software testing requirements.</p></div><div id="course-pills-2"><h4>Exploring AI</h4><ul><li>AI-Initiatives</li><li>The Priority: Excellence</li><li>AI- Intelligence Types</li><li>The Machine Learning Types</li><li>The Quality Learning Initiative</li><li>The Inception in Academics</li><li>AI - Importance &amp; Applications</li><li>The Re-visit</li><li>Learning Re-visited via AI</li><li>Teaching in the world of AI</li><li>Exploring AI for Self-Development</li><li>AI In Academics Beyond Academics</li></ul><h4>Introduction to Machine Learning</h4><ul><li>What is Machine Learning?</li><li>Why Machine Learning?</li><li>Examples - Algorithms behind Machine Learning</li></ul><h4>Introduction to Deep Learning</h4><ul><li>What is Deep Learning?</li><li>Why Deep Learning?</li><li>Example - Deep Learning Vs Machine Learning</li></ul><h4>Introduction to Data Science</h4><ul><li>What is Data Science?</li><li>Why Data Science?</li><li>Examples - Use Cases of Data Science</li></ul><h4>Artificial Intelligence (AI) in Software Testing</h4><ul><li>What is AI in Software Testing?</li><li>The Role of AI Testing</li><li>Why do we Need AI in Software Testing?</li><li>Pros and Cons of AI in Software Testing</li><li>Applications of AI in Software Testing</li><li>Is it time for Testers or QA Teams to worry about AI?</li><li>Automated Testing with Artificial Intelligence</li></ul><h4>Implementing AI in Test Automation</h4><ul><li>Training the AI Bots</li><li>Challenges with AI-powered Applications</li><li>Examples - Real World use cases using Artificial Intelligence</li><li>Demo - Facial Emotion Detection Using Artificial Intelligence</li><li>Demo - Text Analysis API Using Artificial Intelligence</li><li>Demo - EYE SPY Mobile App Using Artificial Intelligence</li></ul><h4>Innovative AI Test Automation Tools for the Future</h4><ul><li>Tools used for Implementing AI in Automation Testing</li><li>What is NEXT?</li><li>AI Test Automation Demo using Testim</li></ul></div>

Nexus Human

Action Learning Facilitator

API Testing

Software Testing & QA

Automation Testing

Microsoft AI-900

AI (Artificial Intelligence)

Implementing AI in Software Testing | AI in Test Automation (TTAI2140)

<p>Feeling Stuck in Your Career? The <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic - 30 in 1 Courses</strong> Bundle 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 Mechanical Engineering bundle provides an engaging learning experience, helping you learn skills across various disciplines.</p><p>With the <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic</strong>Bundle, 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 Mechanical Engineering 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 Mechanical Engineering bundle on your job life and career growth!</p><p><strong>This Mechanical Engineering with Mechatronics Engineering for Mechanic</strong><strong>Bundle Comprises the Following CPD Accredited Courses:</strong></p><ul><li><strong>Course 01: </strong>Mechanical Engineering</li><li><strong>Course 02: </strong>Engineering Mechanics Course for Beginners</li><li><strong>Course 03: </strong>Crack Your Mechanical Engineer Interview</li><li><strong>Course 04: </strong>Automotive Engineering: Onboard Diagnostics</li><li><strong>Course 05: </strong>Automotive Design</li><li><strong>Course 06: </strong>Hybrid Vehicle Expert Training</li><li><strong>Course 07: </strong>Engine Lubrication Systems Online Course</li><li><strong>Course 08: </strong>Rotating Machines</li><li><strong>Course 09: </strong>Electric Circuits for Electrical Engineering</li><li><strong>Course 10: </strong>Electrical Machines for Electrical Engineering</li><li><strong>Course 11: </strong>Electronic &amp; Electrical Devices Maintenance &amp; Troubleshooting</li><li><strong>Course 12: </strong>Digital Electric Circuits &amp; Intelligent Electrical Devices</li><li><strong>Course 13: </strong>MATLAB Simulink for Electrical Power Engineering</li><li><strong>Course 14: </strong>Power Electronics for Electrical Engineering</li><li><strong>Course 15: </strong>Energy Engineer Course</li><li><strong>Course 16: </strong>Energy Saving in Electric Motors</li><li><strong>Course 17: </strong>Electric Vehicle Battery Management System</li><li><strong>Course 18: </strong>A complete course on Turbocharging</li><li><strong>Course 19: </strong>Heating Ventilation and AirConditioning (HVAC) Technician</li><li><strong>Course 20: </strong>Python Intermediate Training</li><li><strong>Course 21: </strong>Spatial Data Visualisation and Machine Learning in Python</li><li><strong>Course 22: </strong>Data Center Training Essentials: Mechanical &amp; Cooling</li><li><strong>Course 23: </strong>Career Development Plan Fundamentals</li><li><strong>Course 24: </strong>CV Writing and Job Searching</li><li><strong>Course 25: </strong>Learn to Level Up Your Leadership</li><li><strong>Course 26: </strong>Networking Skills for Personal Success</li><li><strong>Course 27: </strong>Ace Your Presentations: Public Speaking Masterclass</li><li><strong>Course 28:</strong> Learn to Make a Fresh Start in Your Life</li><li><strong>Course 29: </strong>Motivation - Motivating Yourself &amp; Others</li><li><strong>Course 30: </strong>Excel: Top 50 Microsoft Excel Formulas in 50 Minutes!</li></ul><p><strong>What will make you stand out?</strong></p><p>Upon completion of this online <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic</strong>Bundle, you will gain the following:</p><ul><li>CPD QS Accredited Proficiency with this Mechanical Engineering bundle</li><li>After successfully completing the Mechanical Engineering 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 Mechanical Engineering bundle</li><li>The online test with immediate results</li><li>You can study and complete the Mechanical Engineering bundle at your own pace.</li><li>Study for the Mechanical Engineering bundle using any internet-connected device, such as a computer, tablet, or mobile device.</li></ul><p>The <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic</strong>bundle 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 <strong>Mechanical Engineering bundle</strong>, a comprehensive complete collection of 30 courses. Each course in the Mechanical Engineering 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 Mechanical Engineering bundle offers the flexibility and convenience to learn at your own pace. Make the Mechanical Engineering package your trusted partner in your lifelong learning journey.</p><h2>
        
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                                        300 CPD hours / points
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                                            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 <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic</strong>bundle is perfect for:</p><ul><li>Expand your knowledge and skillset for a fulfilling career with the Mechanical Engineering bundle.</li><li>Become a more valuable professional by earning CPD certification and mastering in-demand skills with the Mechanical Engineering bundle.</li><li>Discover your passion or explore new career options with the diverse learning opportunities in the Mechanical Engineering bundle.</li><li>Learn on your schedule, in the comfort of your home - the Mechanical Engineering bundle offers ultimate flexibility for busy individuals.</li></ul>
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        <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 <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic </strong>bundle curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment.</p>
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    </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 <strong>Mechanical Engineering with Mechatronics Engineering for Mechanic</strong>bundle.</p>
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                                    <h4>CPD Certificates</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Compliance Central

Mechanical Engineering with Mechatronics Engineering for Mechanic - 30 in 1  Courses

<p>Unlock your potential and achieve your goals with our comprehensive online Foundations of Artificial Intelligence  courses! Dive into a world of knowledge and skill enhancement right from the comfort of your own home. Whether you're looking to advance your career, explore a new hobby, or simply expand your horizons, our Foundations of Artificial Intelligence courses are designed to empower you every step of the way.</p><p><strong>Key Features</strong></p><p>The Foundations of Artificial Intelligence course includes the following offers: </p><ul><li>CPD Quality Standards Accredited Foundations of Artificial Intelligence Course</li><li>Lifetime Access</li><li>No Hidden Fees</li><li>24/7 Tutor Support </li><li>Certificate Available with reasonable price</li></ul><p>Discover a wealth of learning opportunities with our diverse range of online courses including Foundations of Artificial Intelligence. From professional development to personal enrichment, we offer courses in a variety of subjects including business, technology, arts, languages, and more. This Foundations of Artificial Intelligence course is carefully curated by industry experts and delivered through engaging multimedia content, interactive quizzes, and hands-on projects to ensure an immersive learning experience.</p><p><strong>Curriculum</strong></p><ul><li><strong>Module 01: Introduction To Artificial Intelligence</strong></li><li><strong>Module 02: Mathematics For AI</strong></li><li><strong>Module 3: Knowledge Representation In AI - Part 1</strong></li><li><strong>Module 4: Knowledge Representation In AI - Part 2</strong></li><li><strong>Module 5: Machine Learning - Part 1</strong></li><li><strong>Module 6: Machine Learning - Part 2</strong></li><li><strong>Module 7: Deep Learning</strong></li><li><strong>Module 8: Natural Language Processing</strong></li><li><strong>Module 9: Computer Vision</strong></li><li><strong>Module 10: Robotics</strong></li><li><strong>Module 11: Building AI Applications</strong></li></ul><p><strong>Why This Online Course is Better for You:</strong></p><p>Unlike traditional classroom settings, our online Foundations of Artificial Intelligence course offers flexibility and convenience tailored to fit your busy lifestyle. With 24/7 access to Foundations of Artificial Intelligence course materials and the ability to learn at your own pace, you can study whenever and wherever it's most convenient for you. Plus, our online platform provides a supportive community of fellow learners and dedicated instructors to guide you every step of the way.</p><h2>
        
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                                        10 CPD hours / points
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                            <p>Our Foundations of Artificial Intelligence course is designed for anyone eager to expand their knowledge and skills, regardless of their background or experience level. Whether you're a working professional looking to upskill, a student seeking supplemental education, or an enthusiast pursuing a passion project, our courses are accessible to learners of all ages and abilities.</p>
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                            <p>Invest in your future and unlock new career opportunities with our online Foundations of Artificial Intelligence course. Gain valuable skills and credentials that are in high demand in today's competitive job market. Whether you're looking to switch careers, climb the corporate ladder, or start your own business, This course provide the expertise and certification you need to succeed.</p>
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        <div>Certificates</div>
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                                    <h4> Digital certificate</h4>
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            Digital certificate -
            £9
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        <h4> Hard copy certificate</h4>
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            Hard copy certificate -
            £15
        </p>
            <div><p>A Hardcopy Certificate (international): £15 + £10 (postal charge) = £25</p></div>

                        </div>

One Education

Foundations of Artificial Intelligence Course

<p><strong>R Programming for Data Science </strong></p><p>Tired of browsing and searching for a <strong>R Programming </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 R Programming bundle has been designed by industry experts. We prioritised what learners were looking for in a complete package and developed this in-demand R Programming course that will enhance your skills and prepare you for the competitive job market.</p><p>Also, ourexperts are available for answering your queries on R Programmingand help you along your learning journey. Advanced audio-visual learning modules of these R Programmingcourses 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 R Programming, 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 R Programming Bundle Consists of the following Premium courses: </strong></p><ul><li><strong> Course 01:</strong> R Programming for Data Science<strong><br /></strong></li><li><strong> Course 02: </strong>Data Analytics with Tableau<strong><br /></strong></li><li><strong> Course 03: </strong>Introduction to Data Analysis<strong><br /></strong></li><li><strong> Course 04: </strong>Data Science with Python<strong><br /></strong></li><li><strong> Course 05: </strong>Data Science &amp; Machine Learning with Python<strong><br /></strong></li><li><strong> Course 06: </strong>Data Structures Complete Course<strong><br /></strong></li><li><strong> Course 07: </strong>Complete Google Analytics Course<strong><br /></strong></li><li><strong> Course 08: </strong>Data Analysis in Excel Level 3 Course<strong><br /></strong></li><li><strong> Course 09: </strong>Basic Google Data Studio<strong><br /></strong></li><li><strong> Course 10: </strong>Data Center Training Essentials: General Introduction<strong><br /></strong></li><li><strong> Course 11: </strong>SQL Programming Masterclass<strong><br /></strong></li><li><strong> Course 12: </strong>Data Analysis in Excel Level 3 Course<strong><br /></strong></li><li><strong> Course 13: </strong>Learn to Use Python for Spatial Analysis in ArcGIS<strong><br /></strong></li><li><strong> Course 14: </strong>GDPR Data Protection Level 5</li></ul><p>Enrol now in R Programming to advance your career, and use the premium study materials from Apex Learning.</p><p>The R Programming for Data Science bundle incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can strengthen your R Programmingexpertise and essential knowledge, which will assist you in reaching your goal.</p><p><strong>Certificate:</strong></p><ul><li><strong>PDF Certificate: Free (Previously it was £6*11 = £66)</strong></li><li><strong>Hard Copy Certificate: Free (For The Title Course: Previously it was £10)</strong></li></ul><h2>
        
        <div>CPD</div>
    </h2>

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                                        140 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>Anyone from any background can enrol in this R Programming for Data Science bundle.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Our R Programming course is fully compatible with PCs, Macs, laptops, tablets and Smartphone devices.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

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

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

                        </div>

R Programming for Data Science

Data Science

This course for absolute beginners provides you with the opportunity to systematically learn core statistical and probability concepts, descriptive statistics, hypothesis testing, regression analysis, analysis of variance (ANOVA), and advanced regression/ML methods such as logistics regressions, polynomial regressions, decision trees, and more.

Statistics and Mathematics for Data Science and Data Analytics

<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>

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

Python

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

Machine Learning (ML)

Machine Learning for Apps Level 4

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

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

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

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

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

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

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

Python Machine Learning Crash Course for Beginners

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

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

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

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

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

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

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

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

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

Machine Learning for Absolute Beginners - Level 2

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

TensorFlow

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

The Complete Machine Learning Course with Python

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

Learning To Learn

Machine Learning - Use in Flutter

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

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

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

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

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

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

Deep Learning

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

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