Name: Big Data Analytics with PySpark Power BI and MongoDB
Price: 29 GBP
Availability: InStock

<p><strong>*** Software Engineering - Software Development ***</strong></p><p>Welcome to the Ultimate Software Development Online Course Bundle! Whether you're a beginner eager to start your programming adventure or a seasoned developer looking to level up your skills, this bundle is your passport to becoming a software wizard!</p><p>Are you ready to embark on a transformative journey into the world of code and creativity?</p><p>Enrol NOW!!!</p><p>This power-packed <strong>Software Engineering - Software Development </strong>bundle comprises our 19 best-selling software development courses:</p><ul><li>Course 1: How To Startup Your Own SaaS (Software As a Service) Company (SaaS Evolution)</li><li>Course 2: Diploma in Python Programming</li><li>Course 3: The Complete Full-Stack JavaScript Course!</li><li>Course 4: Mastering Java for Beginners &amp; Experts</li><li>Course 5: C++ Programming Certificate Course</li><li>Course 6: HTML and CSS Coding: Beginner to Advanced</li><li>Course 7: Building Web Applications with Django and PostgreSQL</li><li>Course 8: SQL NoSQL Big Data and Hadoop</li><li>Course 9: Advanced Diploma in PHP Web Development with MySQL, GitHub &amp; Heroku</li><li>Course 10: Easy to Advanced Data Structures</li><li>Course 11: Web Application Penetration Testing</li><li>Course 12: Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li>Course 13: Azure Administrator Essentials</li><li>Course 14: Amazon Web Services (AWS)</li><li>Course 15: Cyber Security Advanced Training</li><li>Course 16: Flutter &amp; Dart Development for Building iOS and Android Apps</li><li>Course 17: Project Management</li><li>Course 18: Machine Learning for Apps</li><li>Course 19: Internet of Things</li></ul><p><strong>What's more in this Software Engineering - Software Development Bundle?</strong></p><ul><li><strong>Quality License Scheme (QLS) </strong>endorsed<strong> Certificate of Completion,</strong> recorgnised globally</li><li><strong>CPD QS</strong> accredited <strong>Certificate of Completion,</strong> recorgnised globally</li><li>Dedicated Expert Support</li><li>1735 CPD points</li><li>FREE Assessment on all the Software Engineering - Software Development Bundle Courses</li><li>Lowest Price Guarantee Worldwide</li></ul><p><strong>Software Engineering - Software Development</strong></p><p>From mastering the Software Development fundamentals with Python, JavaScript, and Java to creating stunning web applications using HTML, CSS, and React, this bundle covers it all. You'll dive deep into mobile app development with Flutter and React Native, becoming proficient in both iOS and Android platforms.</p><p>By the end of this course bundle, you'll emerge as a seasoned Software Development professional with a diverse skill set, ready to tackle any digital challenge. Don't miss this opportunity to embark on a transformative journey into the world of software mastery. Enrol today!</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        1735 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p><strong>Software Engineering - Software Development</strong></p><p>Are you curious about the ever-evolving world of software development? Do you aspire to become a coding virtuoso or a tech-savvy wizard? If you resonate with any of the following descriptions, then this course is tailor-made for you:</p><ul><li>Beginners in Software Development</li><li>Aspiring Developers</li><li>Career Changers</li><li>Experienced Developers</li><li>Entrepreneurs and Innovators</li><li>Tech Enthusiasts</li><li>Professionals in IT and Tech</li><li>Students and Academics</li><li>Lifelong Learners</li><li>Global Learners</li></ul><p>No matter where you stand on your journey, our "Comprehensive Software Development Mastery Bundle" welcomes you to explore, learn, and master the art of software development. Join us and embark on a transformative learning adventure today!</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Embarking on a journey of software development mastery is an exciting endeavor, and to help you succeed, we've designed this course bundle with minimal prerequisites. Here are the fundamental requirements:</p><ul><li>Basic Computer Skills</li><li>Internet Access</li><li>A Computer</li><li>Passion and Curiosity</li><li>Determination and Patience</li><li>No Prior Coding Experience Required</li><li>Hardware for Coding</li></ul><p>Remember, everyone starts somewhere, and this course bundle is designed to be accessible to a wide range of learners. It's a fantastic opportunity to embark on a transformative journey into the world of software development, regardless of your current skill level.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>The world of software development offers a vast and exciting array of career paths, each with its unique challenges and rewards.</p><ul><li>Software Developer/Engineer</li><li>Web Developer</li><li>Mobile App Developer</li><li>Data Scientist/Data Analyst</li><li>DevOps Engineer</li><li>Software Architect</li><li>UX/UI Designer</li><li>Quality Assurance (QA) Engineer</li><li>Cloud Engineer/Cloud Architect</li><li>Game Developer</li><li>Product Manager</li><li>Freelancer/Consultant</li></ul>
                        </div>

One Education

Software Engineering - Software Development Course

General Admission

<p><strong>Are you interested in advancing your career in database management? Look no further than our comprehensive MySQL: Database Management course!</strong></p><p>Designed to provide you with the skills and knowledge necessary to work as a database administrator, developer, analyst or engineer, this course covers various topics, including database design, implementation, maintenance, SQL programming, and data analysis.</p><p><br />Our hands-on approach to learning will give you practical experience with MySQL database management, preparing you for success in the workforce. With the high demand for skilled database professionals in finance and healthcare industries, our MySQL: Database Management course is an excellent investment in your future.</p><p><strong>This Training package includes:</strong></p><ul><li>MySQL Database Development with an endorsed certificate</li><li>5 additional courses -</li></ul><ul><ol><li>SQL Server for Beginners</li><li>SQL NoSQL Big Data and Hadoop</li><li>The Complete Nodejs MySQL Login System</li><li>Complete Python Machine Learning &amp; Data Science Fundamentals</li><li>Microsoft Access Databases Forms and Reports</li></ol></ul><p>Dedicated tutor support and 24/7 customer support are available to all students with this premium quality MySQL: Database Management bundle.</p><p>This premium online course titled MySQL: Database Management bundle ensures your professional development with endorsement from Quality Licence Scheme in addition to CPD QS accreditation.</p><p>With this training program, success becomes a lot easier, enabling you to monetise your skills. This MySQL: Database Management bundle is designed for both part-time and full-time students and can be completed at a pace that suits your learning style.</p><p><strong>Key Benefits</strong></p><ul><li>Eligibility for CPD QS accredited certificates on successful completion</li><li>Learning materials contain engaging audio-visual elements for your comfort</li><li>Freedom to study at your own pace</li><li>24/7 access to the materials for 12 months</li><li>Full Tutor support on weekdays (Monday - Friday)</li></ul><p><strong>Course Curriculum:</strong></p><p>Here is a curriculum breakdown of the MySQL Database Development:</p><ul><li>Unit 01: Introduction</li><li>Unit 02: Manipulating Tables and Data - CRUD Operations</li><li>Unit 03: Relationships and Foreign Keys</li><li>Unit 04: Aggregate Functions</li></ul><p><strong>How is the MySQL: Database Management bundle assessed?</strong></p><p>To simplify the procedure of evaluation and accreditation for learners, we provide an automated assessment system. Upon completion of an online module, you will immediately be given access to a specifically crafted MCQ test. The results will be evaluated instantly, and the score will be displayed for your perusal. For each test, the pass mark will be set to 60%.</p><p>When all tests have been successfully passed, you will be able to claim a certificate endorsed by the Quality Licence Scheme.</p><p><strong>Certificate of Achievement</strong></p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme</strong></p><p>After successfully completing the MySQL: Database Management bundle, learners will get an endorsed certificate (included with the course) delivered right to their doorstep by post, without paying a single penny! Note that learners residing outside the UK have to pay a £10 postage charge.</p><p><strong>For Additional Courses</strong></p><p>For the Quality Licence Scheme endorsed certificates from the additional courses, you have to pay the fees based on the level of each course:</p><ul><li>Level 1 - £39</li><li>Level 2 - £69</li><li>Level 3 - £79</li><li>Level 4 - £89</li><li>Level 5 - £109</li><li>Level 6 - £119</li><li>Level 7 - £129</li></ul><p><strong>Accredited Certificate</strong></p><p>After successfully completing the courses under this MySQL: Database Management bundle, you will also qualify for CPD QS Accredited Certificates for each of the courses. A certificate is available in PDF format at the cost of £9, or a hard copy can be sent to you via post at £15.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>The MySQL: Database Management bundle is ideal for highly motivated individuals who want to enhance their professional skills and train for the job they want!</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal entry requirements for the MySQL: Database Management bundle, with enrolment open to anyone!<strong> Anyone and everyone</strong> with a knack for learning can enrol on this course without any hesitation.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Studying the MySQL: Database Management bundle is designed to help you get the job of your dreams or even that promotion you've always wanted. Jobs you can explore such as:</p><ul><li>Database Administrator (DBA)</li><li>Database Developer</li><li>Data Analyst</li><li>Database Architect</li><li>MySQL Developer</li><li>Data Engineer</li><li>Business Intelligence Analyst</li><li>Database Manager</li><li>Database Engineer</li><li>Database Consultant</li></ul>
                        </div>

MySQL: Database Management Course

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Have you ever been intrigued by the power behind the most revolutionary software applications? Do you wonder how these applications transform from mere ideas into functional products that ease life&#39;s tasks?</strong> Dive deep into the world of software development with our comprehensive "Software Development Career Track" bundle of <strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">twenty</strong> courses! Within this bundle, you&#39;ll traverse from the fundamentals of Coding with Scratch to advanced JavaScript Python and even delve into Game Development using Unity 3D. Explore the wonders of Web Design, dive into Data Analytics with SQL, and embrace the intricacies of Kotlin, the Android coding bible. There&#39;s no stone left unturned, with courses covering the depths of PHP, the world of AWS SageMaker Canvas, and mastering the essentials of Linux Security. Your software development journey starts here.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Unlock the future of software development today! Embark on a journey that transforms your curiosity into skills. Don&#39;t just dream, build. Enrol now and shape tomorrow&#39;s digital world!</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Software Development Courses List:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 1: </strong>Coding with Scratch</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 2: </strong>C++ Development: The Complete Coding Guide</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 3: </strong>Basic HTML 5</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 4: </strong>JavaScript Advanced Training</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 5: </strong>Python Advanced Training</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 6: </strong>The Complete SQL from Scratch: Bootcamp</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 7: </strong>Easy to Advanced Data Structures</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 8: </strong>SQL for Data Science, Data Analytics and Data Visualization</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 9: </strong>Learn Web Development from Scratch</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 10: </strong>Web Design for Beginners: Build Websites in HTML &amp; CSS</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 11: </strong>Kotlin Masterclass Programming Course: Android Coding Bible</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 12: </strong>jQuery Masterclass Course: JavaScript and AJAX Coding Bible</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 13: </strong>Secure Programming of Web Applications - Developers and TPMs</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 14: </strong>Developing Game in Unity 3D</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 15: </strong>Game Development using Cocos2d-x v3 C++</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 16: </strong>PHP for Beginners 2023 Guide: Develop an ECommerce Store</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 17: </strong>PHP for Beginners: PDO Crash Course 2021</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 18: </strong>Machine Learning Model Using AWS SageMaker Canvas</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 19: </strong>WordPress Security Mastery</li><li fr-original-style="" style="box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Course 20: </strong>Linux Security and Hardening</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">Learning Outcomes:</strong></p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Gain a foundational understanding of programming languages like Scratch, C++, HTML5, JavaScript, Python, and Kotlin.</li><li fr-original-style="" style="box-sizing: border-box;">Understand the intricacies of web development and design, ranging from basic website development to e-commerce platform creation.</li><li fr-original-style="" style="box-sizing: border-box;">Delve into data structures, SQL&#39;s utility in data science, analytics, and visualisation.</li><li fr-original-style="" style="box-sizing: border-box;">Master game development techniques using Unity 3D and Cocos2d-x v3 C++.</li><li fr-original-style="" style="box-sizing: border-box;">Acquire knowledge on machine learning with AWS SageMaker Canvas.</li><li fr-original-style="" style="box-sizing: border-box;">Grasp essential security aspects from WordPress protection to Linux hardening.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">This dynamic Software Development Career Track bundle serves as a ladder, guiding you through the steps of software development. Starting with the foundational elements, you&#39;re introduced to Scratch&#39;s simplistic yet effective world, gradually escalating to the dynamic languages like C++ and Kotlin. As you advance, delve into the vibrant realm of web development and design, unlocking the potential to craft and curate seamless websites using HTML &amp; CSS, paired with interactive features facilitated by JavaScript and jQuery.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Transition from web design to data with courses focusing on SQL&#39;s various applications, be it in straightforward data structures or the vast domains of science, analytics, and visualisation. But software development isn&#39;t just about creating - it&#39;s also about protecting. Hence, our curated content ensures you grasp the nuances of securing digital entities, from WordPress platforms to entire Linux systems. And for those with a passion for animation and virtual worlds, the game development courses promise a thrilling voyage into Unity 3D and Cocos2d-x v3 C++ realms.</p><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for" fr-original-style="" style="box-sizing: border-box;">
                            <ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Aspiring software developers keen on understanding the broader spectrum of the field.</li><li fr-original-style="" style="box-sizing: border-box;">Web designers and developers aiming to expand their skill set.</li><li fr-original-style="" style="box-sizing: border-box;">Data enthusiasts wanting to harness the power of SQL in analytics.</li><li fr-original-style="" style="box-sizing: border-box;">Game developers aspiring to master tools like Unity 3D.</li><li fr-original-style="" style="box-sizing: border-box;">IT professionals keen on deepening their knowledge in security and hardening.</li></ul>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Requirements</div>
    </h2>

                        <div id="requirements" fr-original-style="" style="box-sizing: border-box;">
                            <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Without any formal requirements, you can delightfully enrol in this course.</p>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Career path</div>
    </h2>

                        <div id="career_path" fr-original-style="" style="box-sizing: border-box;">
                            <ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Software Developer</li><li fr-original-style="" style="box-sizing: border-box;">Web Developer</li><li fr-original-style="" style="box-sizing: border-box;">Data Analyst</li><li fr-original-style="" style="box-sizing: border-box;">Game Developer</li><li fr-original-style="" style="box-sizing: border-box;">Security Specialist</li><li fr-original-style="" style="box-sizing: border-box;">WordPress Developer</li><li fr-original-style="" style="box-sizing: border-box;">Linux System Administrator</li></ul>
                        </div><h2 fr-original-style="" style="box-sizing: border-box;">
        
        <div fr-original-style="" style="box-sizing: border-box;">Certificates</div>
    </h2>

                        <div id="optional_extras" fr-original-style="" style="box-sizing: border-box;">
                                    <h4 fr-original-style="" style="box-sizing: border-box;">CPD Certificate Of Completion</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Digital certificate -
            Included
        </p>
            <div fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">20 Digital Certificates Are Included With This Bundle</p></div>
        <h4 fr-original-style="" style="box-sizing: border-box;">CPD Quality Standard Hardcopy Certificate (FREE UK Delivery)</h4>
        <p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">
            Hard copy certificate -
            &pound;9.99
        </p>
            <div fr-original-style="" style="box-sizing: border-box;"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Hardcopy Transcript: &pound;9.99</p></div>

                        </div>

NextGen Learning

Learning & Development

Online Course Creation

Computing & Software Development Tools

Digital Skills

Software Development

Continuous Professional Development (CPD)

Software Development Career Track - 20 Courses Bundle

<p>Did you know that Python is one of the most widely used programming languages in the world? In fact, according to a recent survey, Python was ranked as the third most popular programming language in the world, behind only Java and C. That's why learning Python programming can be a smart move for anyone who wants to stay relevant and competitive in the tech industry.</p> <p>If you're a non-programmer looking to learn Python programming, QLS offers a Level 5 course that includes both Levels 3 and 5 curriculum. With this comprehensive course, you'll learn everything from the basics of Python programming to more advanced topics that will help you become proficient in the language. And, since the course is endorsed by Quality Licence Scheme, you can be sure that you're getting a quality education.</p> <p>But that's not all - when you enrol in this Python programming course, you'll also receive five career guided courses for free. These courses cover topics like career development, networking skills, boosting your confidence and self-esteem, CV writing, and stress management. With this comprehensive package, you'll not only learn valuable programming skills but also gain the soft skills you need to succeed in any industry.</p> <p>And the best part? When you complete the Python programming course, you'll receive a <strong>Level 5 Endorsed certificate</strong> hardcopy from Quality Licence Scheme. This certificate will be a valuable addition to your resume and will show employers that you have the skills and knowledge to excel in the tech industry.</p> <p>So why wait? Enrol in QLS's Python Programming for Non-Programmers Level 3 &amp; 5 course today and take the first step towards a brighter future.</p> <p><strong>Learning Outcomes:</strong></p> <ul> <li>Develop a comprehensive understanding of Python programming, including its syntax and basic concepts.</li><li>Apply knowledge of conditional branching and data storage structures, such as tuples, lists and dictionaries, in Python programming.</li><li>Demonstrate proficiency in strings operations, date and time manipulation, and file handling using Python.</li><li>Create user-defined functions and understand their role in efficient programming.</li><li>Explore advanced topics, including importing external/internal libraries, sending emails, and handling exceptions in Python.</li><li>Utilise installation and scheduling techniques in Python programming and gain knowledge of running programs from the command prompt and Jupyter Notebook.</li><li>Develop a thorough understanding of database management using SQLite in Python programming.</li></ul> <p><strong>Please Note: </strong>Imperial Academy is a One Education approved resale partner for Quality Licence Scheme Endorsed courses.</p><p><strong>Diploma in </strong><strong>Python Programming for Non-Programmers Level 3 &amp; 5 at QLS </strong><strong>Description: </strong></p> <p><strong>In Level 3</strong> of QLS's Python Programming for Non-Programmers course, learners will gain a foundational understanding of Python programming, including the syntax and basic concepts. They will learn about conditional branching, data storage structures such as tuples, lists and dictionaries, strings operations, date and time manipulation, file handling and user-defined functions in Python programming. Additionally, they will work on a project that applies the concepts covered in the course, the "Rock Paper and Scissors" game</p> <p><strong>In Level 5</strong> of the course, learners will delve deeper into the intricacies of Python programming. They will learn how to import external/internal libraries, send emails, and handle exceptions in Python. They will also gain knowledge of installation and scheduling techniques, including running programs from the command prompt and Jupyter Notebook. Furthermore, they will learn about database management using SQLite in Python programming. The course will conclude with a summary of the topics covered and their relevance in the tech industry.</p> <p><strong>This bundle comprises the following courses:</strong></p> <p><strong>QLS Endorsed Course:</strong></p> <ul> <li><strong>Course 01:</strong> Diploma in Python Programming at QLS Level 5</li></ul> <p><strong>CPD QS Accredited Courses:</strong></p> <ul> <li><strong>Course 01:</strong> Career Development Plan Fundamentals</li><li><strong>Course 02:</strong> Networking Skills for Personal Success</li><li><strong>Course 03:</strong> Boost Your Confidence and Self-Esteem</li><li><strong>Course 04:</strong> CV Writing and Job Searching</li><li><strong>Course 05:</strong> Learn to Reduce and Manage Stress</li></ul> <p><strong>Certificate</strong></p> <p>In pursuing this QLS-endorsed course, learners attain a profound understanding and amplify their professional prowess, enriching their curriculum vitae.</p> <p>The Quality Licence Scheme stands as a reputable and distinguished endorsement, emblematic of exceptional standards and excellence throughout the UK.</p> <p>Upon completion, learners will receive an Diploma in Python Programming at QLS Level 5 Endorsed hard copy certificate, making it easy for them to showcase their achievements.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                             <ul> <li>Individuals who have no prior programming experience and want to learn Python programming.</li><li>Non-programmers who wish to expand their skill set and remain competitive in the tech industry.</li><li>Professionals seeking to improve their data manipulation and analysis skills.</li><li>Students pursuing a career in computer science or data analysis.</li><li>Entrepreneurs who wish to develop their own applications.</li></ul> 
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <ul> <li>Python Developer: £25,000 - £70,000 per year.</li><li>Data Analyst: £20,000 - £50,000 per year.</li><li>Software Engineer: £30,000 - £90,000 per year.</li><li>Web Developer: £20,000 - £50,000 per year.</li><li>Business Intelligence Analyst: £25,000 - £60,000 per year.</li><li>Artificial Intelligence Engineer: £45,000 - £100,000 per year.</li><li>Machine Learning Engineer: £50,000 - £120,000 per year.</li></ul> 
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Imperial Academy

Level 5 QLS Endorsed Course | Endorsed Certificate Included | Plus 5 Career Guided Courses | CPD Accredited

Python Programming for Non-Programmers Level 3 & 5 at QLS

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p>This course is intended for:<br />A technical audience at an intermediate level</p>Overview<p>Using Amazon SageMaker, this course teaches you how to:<br />Prepare a dataset for training.<br />Train and evaluate a machine learning model.<br />Automatically tune a machine learning model.<br />Prepare a machine learning model for production.<br />Think critically about machine learning model results</p><p>In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment.</p></div><div id="course-pills-2"><h4>Day 1</h4><ul><li>Business problem: Churn prediction<br />Load and display the dataset<br />Assess features and determine which Amazon SageMaker algorithm to use<br />Use Amazon Sagemaker to train, evaluate, and automatically tune the model<br />Deploy the model<br />Assess relative cost of errors</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Practical Data Science with Amazon SageMaker training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the Practical Data Science with Amazon SageMaker course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Nexus Human

Data Engineering

Data Science

Data Skills

Trainers Course

Practical Data Science with Amazon SageMaker

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					<div><p><span>Kickstart your career with our </span><b>Complete Python from Scratch: Start your career in Python 3+ </b><span>course. Python is an all-purpose language with one of the biggest and abundant library features. It is used </span><span>for</span><span> a wide range of purposes such as web development, scripting, testing, app development, and data science. So it's one of the most sought after skills by employers.</span></p></div>
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					<div><p><span>The </span><b>Complete Python from Scratch: Start your career in Python 3+ </b><span>course</span> <span>is designed to give you a complete understanding of the programming language right from setup to advanced level applications.The experience will provide you with the chance to work in a variety of sectors including web development, machine learning, data security, analytics and so much more.</span></p><p><span>It will prepare you with sound theoretical and practical knowledge of Python programming that will prepare you to work with evidence-based strategies. If you are keen to equip yourself with knowledge of programming with Python and make a strategic career intervention</span><span>,</span><span> then choose our </span><b>Complete Python from Scratch: Start your career in Python 3+ </b><span>course.</span></p><p><span>Upon completion of this CPD accredited course, you will be awarded a certificate of completion, as proof of your expertise in this field, and you can show off your certificate in your LinkedIn profile and in your resume to impress employers and boost your career.</span></p><p><span>Our </span><b>Complete Python from Scratch: Start your career in Python 3+ </b><span>course is packed with </span><b>14 </b><span>modules</span><span>, with</span><span> a total of 18 hours of learning materials. You will be able to study this course at your own pace, from anywhere and at any time. Enrol today and upgrade your knowledge on </span><span>Python programming</span><span> to lead a more prosperous life.</span></p></div>
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Course Cloud

Complete Python from Scratch: Start your career in Python 3+

<p><strong>** Enjoy an EXCLUSIVE 20% OFF. GET the QLS-Endorsed course for ONLY £119 **</strong></p><p>Do you have an excellent head for numbers, a keen mind, and an insatiable desire to learn? A career as a data analyst may be ideal for you. Data analysts transform raw numerical and statistical information into knowledge that can be applied to problem-solving and keeping tabs on corporate operations. They analyse data to create readable charts, tables, and reports.</p><p>This Data Analyst Bundle has all the necessary topics covered so that you shine in your career. So, ENROL NOW.</p><p><strong>Learning Outcome</strong></p><ul><li>Learn the fundamentals of business process management in detail</li><li>Enhance the skills and knowledge required to use data to drive business improvement</li><li>Explore the best Data analyst tools and processes</li><li>Learn the procedures to create a Pareto chart in Excel</li><li>Get excellent tips from experts on how to present your performance data</li><li>Have a better understanding of the properties of histograms and how to interpret one</li><li>Discover the numerous metrics of variability used in Data analyst</li><li>Be competent in using the consumer's voice to improve the customer experience</li></ul><p><strong>QLS Endorsed Course:</strong></p><ul><li>Course 01: Data Analysis In Excel at QLS Level 5</li></ul><p><strong>â </strong><strong>11 FREE CPD Courses </strong><strong>â</strong></p><ul><li>Course 01: Financial Modelling Using Excel</li><li>Course 02: Excel spreadsheet for Beginner</li><li>Course 03: VLOOKUP: Master Excel Formula VLOOKUP</li><li>Course 04: Microsoft Excel: Master Power Query in 120 Minutes!</li><li>Course 05: Excel Add-in with C# VSTO and Web</li><li>Course 06: The Complete Microsoft Outlook Masterclass</li><li>Course 07: Microsoft Access Tables and Queries</li><li>Course 08: Microsoft Teams for Managers</li><li>Course 09: Touch Typing Essentials</li><li>Course 10: Time Management</li><li>Course 11: Certificate in CV Writing and Interview Skills</li></ul><p>Excel data analysis and financial modelling, VLOOKUP, C# VSTO, Microsoft Outlook, Microsoft Access tables and queries, and Microsoft Teams for Managers are the highlights of the topics covered in the 14 courses included in the Bundle. As a bonus, you'll learn to maximise your time and do twice as much. What's more, you'll learn how to make the most of your influence at work. Finally, we discuss the importance of perfecting your interview skills and providing a well-written resume to land the positions of your dreams.</p><p><strong>Course Curriculum:</strong></p><p><strong>Data Analysis In Excel at QLS Level 5</strong></p><ul><li>Module 01: Introduction</li><li>Module 02: Agenda and Principles of Process Management</li><li>Module 03: The Voice of the Process</li><li>Module 04: Working as One Team for Improvement</li><li>Module 05: Exercise: The Voice of the Customer</li><li>Module 06: Tools for Data Analysis</li><li>Module 07: The Pareto Chart</li><li>Module 08: The Histogram</li><li>Module 09: The Run Chart</li><li>Module 10: Exercise: Presenting Performance Data</li><li>Module 11: Understanding Variation</li><li>Module 12: The Control Chart</li><li>Module 13: Control Chart Example</li><li>Module 14: Control Chart Special Cases</li><li>Module 15: Interpreting the Control Chart</li><li>Module 16: Control Chart Exercise</li><li>Module 17: Strategies to Deal with Variation</li><li>Module 18: Using Data to Drive Improvement</li><li>Module 19: A Structure for Performance Measurement</li><li>Module 20: Data Analysis Exercise</li><li>Module 21: Course Project</li><li>Module 22: Test your Understanding</li></ul><p><strong>------- 11 Additional FREE CPD Courses --------</strong></p><h2>
        
        <div>CPD</div>
    </h2>

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

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

                        <div id="requirements">
                            <p>Anyone can enrol in this Data Analyst course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

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

                        <div id="optional_extras">
                                    <h4> Hard Copy QLS Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>PDF Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Level 5 QLS Diploma | Free QLS Endorsed Certificate | 11 FREE CPD Courses & PDF Certificates | Tutor Support

Data Analyst Training

<strong>Learn to create captivating AI-generated art using DALL-E, Midjourney, and other AI art software. This course covers the fundamentals of AI art creation and provides hands-on training on how to generate stunning visuals. Develop your creativity and artistic skills in a fun and engaging way.</strong>
        <p>Generative AI has revolutionized the art world by providing an entirely new way to create unique and stunning visuals. In today's age of technology, it's essential to keep up with the latest tools and techniques, and this is where this comprehensive course on AI-generated art comes in.

In this course, you will learn how to create stunning visuals using the latest AI art software, including DALL-E, Midjourney, and other tools. You will begin by learning the basics of generative AI and its applications in art. From there, you will explore various AI art software and learn how to use them to create beautiful pieces of art.

The course will cover everything from generating simple images to creating complex visuals and textures. You will learn how to tweak parameters, adjust styles, and create custom datasets to get the exact output you want. Additionally, you will explore the different types of generative art, including style transfer, GANs, and neural style transfer.

By the end of this course, you will be well-versed in generative AI and its applications in art. You will have a solid understanding of how to use various AI art software, including DALL-E, Midjourney, and others, to create stunning visuals.</p>
        <h2>What You Will Learn</h2><p>Understand the theory behind AI and GPT-3<br>Learn how to construct effective text prompts for AI art<br>Learn different framing techniques for AI-generated art<br>Explore different AI art software and their unique features<br>Implement image URLs for visual inspiration in Midjourney<br>Learn how to perform image masking with DALL-E 2</p>
        <h2>Audience</h2><p>This course is ideal for anyone who is interested in exploring the potential of generative AI art. Whether you are an artist, graphic designer, photographer, or simply a creative person, this course will equip you with the skills and knowledge to produce stunning and unique AI-generated artwork. No prior experience in programming or AI is required as this course is designed for beginners.</p>
        <h2>Approach</h2><p>The learning approach for this course is practical and hands-on. You will learn by doing, with step-by-step guidance on how to create your own AI-generated art using DALL-E and other AI art software.</p>
        <h2>Key Features</h2><p>Advance your skills in generating text prompts and modifiers * Learn how to create stunning visuals with AI-generated art software * Discover techniques to incorporate AI-generated art in your creative work</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Generative-AI-Art-For-Beginners</p>
        <h2>About the Author</h2><h3>Oliver Theobald</h3><p>Oliver Theobald is an accomplished technical writer and the best-selling author of "Machine Learning for Absolute Beginners". With expertise in AI, fintech, and cloud computing, he has worked in various technical roles across the globe. Oliver currently splits his time between China and Japan, where he continues to expand his knowledge and passion for emerging technologies.

As an instructor, Oliver brings a unique approach to teaching complex topics. He believes in cutting through the jargon and presenting information in a clear, concise, and engaging manner. His course content is designed to be easy to understand, yet powerful in its application. Whether you're an absolute beginner or an experienced professional, Oliver's goal is to help you build the skills and knowledge you need to succeed in your field.</p>
        <h2>Course Outline</h2><h4>1. Generative AI Art</h4><p>In this section, we will be introduced to the exciting world of AI-generated art. The section begins with an introduction to the course objectives and an overview of AI theory, including NLP, ML, and supervised learning. You will then learn how to construct a good text prompt for their AI art and explore how framing influences the visible field. The section also covers a tour of AI art software, including Craiyon, Midjourney, and DALL-E 2. You will learn how to use image URLs as visual inspiration, perform image masking, and use parameters to generate unique prompts.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will cover the course learning objective and give you a brief overview of the exciting world of AI-generated art.</p></td></tr><tr><td style='padding: 15px;'><p>2. AI Theory 101</p><p style='margin: 0'>In this video, we will dive into the theory behind AI, including Natural Language Processing (NLP), Machine Learning (ML), and supervised learning. We will also give you a quick introduction to GPT-3, the powerful AI language model that we will be using throughout the course.</p></td></tr><tr><td style='padding: 15px;'><p>3. Welcome to the World of AI Text Prompts</p><p style='margin: 0'>In this video, you will learn how to construct a good text prompt for your AI art. We will cover important aspects such as modifiers, character limit, ideal length, emojis, containers, and context. We will also show you some examples and tips on how to know your subject matter and create a vaguely defined prompt.</p></td></tr><tr><td style='padding: 15px;'><p>4. Framing</p><p style='margin: 0'>In this video, we will explore how framing influences the angle of the visible field and controls how content appears in terms of perspective. We will look at some samples for Point-of-view (POV), over-the-shoulder, long shot (view of a city), and isometric view.</p></td></tr><tr><td style='padding: 15px;'><p>5. Tour of AI Art Software</p><p style='margin: 0'>In this video, we will take you on a tour of different AI art software such as DALL-E 2, Midjourney, StarryAI, NightCafe, stable diffusion, and Craiyon. We will give you an overview of each software's unique features and benefits.</p></td></tr><tr><td style='padding: 15px;'><p>6. Getting Started with Craiyon</p><p style='margin: 0'>In this video, we will have a deeper look at Craiyon, which was formerly known as DALL-E mini. We will cover the advantages and drawbacks of using Craiyon for creating AI-generated art.</p></td></tr><tr><td style='padding: 15px;'><p>7. Getting Started with Midjourney</p><p style='margin: 0'>In this video, we will take a deeper look at Midjourney and learn how to use it effectively. We will explore various features and have a look at some examples to help you get started.</p></td></tr><tr><td style='padding: 15px;'><p>8. Midjourney Image Licensing + Terms of Service</p><p style='margin: 0'>In this video, we will go over Midjourney's terms of service and other important information you should know before using the software. We will cover age requirements, blocked content, rights of use, and privacy policy to ensure that you have a clear understanding of Midjourney's policies.</p></td></tr><tr><td style='padding: 15px;'><p>9. Using Images as Part of Your Prompt</p><p style='margin: 0'>In this video, we will show you how to add one or more image URLs to your prompt in Midjourney. These images will serve as visual inspiration for your prompt and help you generate more creative ideas. We will also provide an example to demonstrate this process.</p></td></tr><tr><td style='padding: 15px;'><p>10. Image Masking with DALL-E 2</p><p style='margin: 0'>In this video, we will demonstrate how to perform image masking and change the background of an image using DALL-E 2. We will walk you through the steps and provide an example to show you how to achieve this effect.</p></td></tr><tr><td style='padding: 15px;'><p>11. Midjourney Parameters</p><p style='margin: 0'>In this video, we will introduce you to Midjourney's parameters, also known as "switches" or "flags", that should be added at the end of your prompt. We will cover negative prompts, dimensions and size, seed numbers, seeds, randomization, video, quality values, private and public mode, and show. We will also provide some examples to help you better understand how to use these parameters.</p></td></tr><tr><td style='padding: 15px;'><p>12. DSNR Bot for Custom Prompts</p><p style='margin: 0'>In this video, we will explore a Discord bot called DSNR, which can analyze your input and generate unique prompts based on your requirements. We will show you how to use this bot and provide some examples to help you get started.</p></td></tr><tr><td style='padding: 15px;'><p>13. Project Time</p><p style='margin: 0'>In this video, we will introduce you to the project that you will be working on as an assignment. We will provide you with some guidelines and tips to help you complete the project successfully.</p></td></tr><tr><td style='padding: 15px;'><p>14. Before You Go</p><p style='margin: 0'>In this video, we will have a look at some more AI options currently available. We will provide you with some resources and information to help you continue exploring AI and its applications.</p></td></tr></tbody></table>

Packt

Video

Web Development

Midjourney

Learn to create captivating AI-generated art using DALL-E, Midjourney, and other AI art software. This course covers the fundamentals of AI art creation and provides hands-on training on how to generate stunning visuals. Develop your creativity and artistic skills in a fun and engaging way.

Generative AI Art For Beginners

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			<p><span>SQL Azure is Microsoft's cloud database service. Based on </span><span>SQL Server</span><span> database technology and built on Microsoft's </span><span>Windows Azure</span><span> cloud computing platform, SQL Azure enables organizations to store relational data in the cloud and quickly scale the size of their databases up or down as business needs change. </span></p>
<p><span>This Azure - SQL focuses primarily on Azure SQL Database as a database service that can quickly and efficiently scale to meet demand, is automatically highly available, and supports a variety of third party software. Learn how to deploy relational and non-relational databases in the cloud and explore the breadth of Azure's data services, from a single database instance to a massive scale data warehouse for working with Big data. You'll gain an understanding of how to configure firewall rules, manage log-ins and users, as well as roles and permissions, perform a database restore, and generally secure an Azure SQL database.</span></p>
<p><span>Finally, you'll be introduced to Azure SQL Data Warehouse, a fully managed and scalable cloud service, that is compatible with other Azure offerings, such as Machine Learning and Data Factory, as well as existing SQL Server tools.</span></p>
<h3><strong>What Will I Learn?</strong></h3>
<ul>
<li><span>Overview and provision Azure SQL</span></li>
<li>Connect to Azure SQL DB and Migrate DB to Azure</li>
<li>Work with SQL security and metrics</li>
<li><span>Configure SQL DB auditing</span></li>
<li><span>Copy and export a database</span></li>
<li><span>Use DB Self-Service Restore and SQL DB Geo-Replication</span></li>
</ul>
<h3><strong>Who is the target audience?</strong></h3>
<ul>
<li>Students wanting an understanding of Azure SQL and to build their skills.</li>
</ul>
<h3><strong>Requirements</strong></h3>
<div>
<ul>
<li>Basic understanding of Azure and SQL concepts</li>
</ul>
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					<td colspan="4">Introduction</td>
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					<td><i></i></td>
					<td>Introduction</td>
					<td><span>FREE</span> </td>
					<td><span><i></i> 00:02:00</span></td>
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					<td colspan="4">Azure SQL</td>
				</tr>
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					<td><i></i></td>
					<td>Azure SQL</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
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					<td><i></i></td>
					<td>Provisioning Azure SQL</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
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					<td><i></i></td>
					<td>Connecting to Azure SQL DB</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
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					<td><i></i></td>
					<td>Migrating to Azure DB</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
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					<td><i></i></td>
					<td>Understanding SQL Security</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
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					<td><i></i></td>
					<td>Understanding SQL DB Metrics and Auditing</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
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					<td><i></i></td>
					<td>Backing Up and Protecting your Database</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
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					<td><i></i></td>
					<td>Using SQL DB Geo-Replication</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
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					<td colspan="4">Conclusion</td>
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					<td>Course Recap</td>
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					<td><span><i></i> 00:02:00</span></td>
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					<td colspan="4">Course Certification </td>
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				</tbody></table>

iStudy UK

Azure SQL Database

Azure Cosmos DB

Microsoft AZ-104

Microsoft Azure

Microsoft AZ-303

Azure Machine Learning

Azure - SQL

SkillWise

Big Data Analytics with PySpark Power BI and MongoDB

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

Microsoft AZ-900

Microsoft AZ-700

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

Python

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

Machine Learning - Use in Flutter

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

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

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

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

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        <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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