Name: Gas Lift Design & Optimization using NODAL Analysis
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<h2 style="color: rgb(15, 21, 26); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(15, 21, 26);">Overview</h2><div style="color: rgb(15, 21, 26); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);"><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Thinking about how AI can work as part of a digital transformation for an organisation? Maybe your senior managers, board or other stakeholders need a clear strategy to convince them about the need before they commit to investing in new technology and processes.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;">A digital ecosystem is fundamental to the operations of any modern business, and with the addition of AI, it has the capability to enable an organisation to service its customers and data better.<br fr-original-style="" style="box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;">This award introduces the critical concepts associated with AI and explores its relationship with the systems and processes that make up the digital ecosystem. It explores how AI can empower organisations to utilise Big Data through the use of Business Analysis and Machine Learning, and encourages candidates to consider a future vision of the world that is powered by AI.<br fr-original-style="" style="box-sizing: border-box;"><br fr-original-style="" style="box-sizing: border-box;">Upon completion of the award, candidates will be able to demonstrate:</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">1. An understanding of the key components of AI</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">2. An understanding of the relationship between AI and The Digital Ecosystem</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">3. An understanding of the relationship between AI and Data</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">4. An understanding of the role the IT professional plays in supporting AI for the future.ž</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;">Why Choose Duco Digital Training?</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">With self-study, you get a whole package of support we call PASS ASSIST, including:<br fr-original-style="" style="box-sizing: border-box;">Free one year BCS Associate membership (normally costing &pound;90)<br fr-original-style="" style="box-sizing: border-box;">Free BCS Data Analyst eBook<br fr-original-style="" style="box-sizing: border-box;">Practice exam papers<br fr-original-style="" style="box-sizing: border-box;">Series of support videos to get the most out of your course, making a learning plan and how to prepare for the exam<br fr-original-style="" style="box-sizing: border-box;">5-star rating<br fr-original-style="" style="box-sizing: border-box;">95% pass rate.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">A live training option is available, please contact us.</p><section style="color: rgb(15, 21, 26); box-sizing: border-box;" id="isPasted" fr-original-style=" color: rgb(15, 21, 26);"><h2 fr-original-style="" style="box-sizing: border-box;">Description</h2><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">The &ldquo;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">AI and the Digital Ecosystem - BCS Foundation Award</strong>&rdquo; is a professional certification that is a globally recognized professional qualification that will help develop your knowledge and understanding of:</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">1. Key components of AI:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Recall the general definition of human intelligence. (see Encyclopaedia Britannica)</li><li fr-original-style="" style="box-sizing: border-box;">State a general definition of artificial intelligence and how it relates to human intelligence.</li><li fr-original-style="" style="box-sizing: border-box;">Describe how the following are used within an application of Artificial intelligence; Machine Learning, Deep Learning, Data Science, Knowlewdge-Based systems , Robotics and Automation.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">2. AI and the digital ecosystem:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Explain what a digital ecosystem is.</li><li fr-original-style="" style="box-sizing: border-box;">Describe an ecosystem mindset.</li><li fr-original-style="" style="box-sizing: border-box;">Identify how AI can facilitate a digital ecosystem that works intuitively.</li><li fr-original-style="" style="box-sizing: border-box;">Discuss why you might make use of AI to create a digital ecosystem as a driver or enabler.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">3. The relationship between AI and Data:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Explain data infrastructure with specific relevance to Big Data</li><li fr-original-style="" style="box-sizing: border-box;">Describe the following concepts within data architecture.</li><li fr-original-style="" style="box-sizing: border-box;">Suggest ways in which AI can support in the utilisation of Big Data.</li><li fr-original-style="" style="box-sizing: border-box;">Recognise different approaches used within the testing of AI systems.</li></ul><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">4. The potential future of AI:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Identify industries that can invest in AI.</li><li fr-original-style="" style="box-sizing: border-box;">Recognise the role of AI in raising human intelligence.</li><li fr-original-style="" style="box-sizing: border-box;">Explain the concept of digital singularity</li><li fr-original-style="" style="box-sizing: border-box;">Explain the need for individuals and organisations to maintain and update their skills and<br fr-original-style="" style="box-sizing: border-box;">knowledge in relation to current and emerging technologies.</li><li fr-original-style="" style="box-sizing: border-box;">Identify the role the IT professional plays in supporting a utopian future with AI.</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;">Exam Information of &ldquo;AI and the Digital Ecosystem - BCS Foundation Award&rdquo; Course</strong>:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">It is a 16 multiple choice questions, 2 scenario-based questions</li><li fr-original-style="" style="box-sizing: border-box;">Pass mark is 13/20 points or 65%</li><li fr-original-style="" style="box-sizing: border-box;">Duration of the exam is 30 minutes</li><li fr-original-style="" style="box-sizing: border-box;">The exam will be supervised virtually</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;">Who is BCS?</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Recognised globally by corporations, academics and IT professionals, BCS is the British Computer Society, more commonly known as the Chartered Institute for IT.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">They have over 60,000 members in 150 countries, and a wider community of business leaders, educators, practitioners and policy-makers all committed to their mission of leading the IT industry through its ethical challenges, to support the people who work in the industry, and to make IT good for society.</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;">What our students say about us:</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">"The training provided will assist me to conduct AI research projects in a more professional and systematic way. The knowledge provided will be incredibly valuable for my future study in the field of computer vision."<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">&nbsp;&mdash; Muhammad Lecturer and AI PhD Candidate</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">&ldquo;Good training materials for remote learning, and the most impressive part was the speed at which they responded to questions.&rdquo;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">&nbsp;&mdash; Tom Data Protection Practitioner</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">"This proved to be a very valuable course to me."&nbsp;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">&mdash; Janet Business Analyst</strong></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;">Meet Our Instructor</strong></p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Darren Winter is Director at Duco Digital and supports organisations with cutting-edge business intelligence, sales, and marketing automation solutions alongside courses and training in artificial intelligence, business analysis, and data protection across diverse sectors spanning the UK, Europe, the Middle East, and SE Asia.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Noteworthy achievements include training academics in AI for the Indonesian government and teaching students in Technology Sales for the Saudi Arabian government. In 2023, the UK Department of Business and Trade invited him as a leaders of AI for a trade mission to Indonesia where he discussed Indonesian AI and ethical policy with key stakeholders.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">He is also studying for his PhD in AI Ethics at Cordoba University, Spain, where he is exploring the impacts of the EU AI Act upon organisations.</p></section><section style="color: rgb(15, 21, 26); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);"><h2 fr-original-style="" style="box-sizing: border-box;">Who is this course for?</h2><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">For whom is the &ldquo;<strong fr-original-style="" style="font-weight: 700; box-sizing: border-box;">AI and the Digital Ecosystem - BCS Foundation Award</strong>&rdquo; course designed?</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Whether you&#39;re new to the field of artificial intelligence or a professional looking to enhance your understanding of AI within the digital ecosystem, the &ldquo;AI and the Digital Ecosystem - BCS Foundation Award&rdquo; course is tailored to meet your needs. It&#39;s designed for:</p><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">Aspiring AI Professionals: Individuals eager to enter the dynamic field of artificial intelligence, ready to establish a solid foundation in AI concepts and applications within the digital landscape.</li><li fr-original-style="" style="box-sizing: border-box;">Tech-Savvy Students: University scholars aiming to complement their academic knowledge with practical, industry-relevant skills in AI, preparing for a successful career in technology.</li><li fr-original-style="" style="box-sizing: border-box;">Career Changers: Professionals considering transitioning into AI roles from diverse backgrounds, seeking the essential knowledge required to thrive in the AI-driven digital ecosystem.</li><li fr-original-style="" style="box-sizing: border-box;">Digital Innovation Enthusiasts: Practitioners passionate about driving innovation and leveraging AI to enhance digital strategies and solutions within their organizations.</li><li fr-original-style="" style="box-sizing: border-box;">Emerging Leaders and Managers: Entry-level managers and team leaders looking to expand their AI expertise, enabling them to support AI initiatives and effectively integrate AI technologies into their organizational strategies.</li></ul></section><section style="color: rgb(15, 21, 26); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);"><h2 fr-original-style="" style="box-sizing: border-box;">Requirements</h2><ul fr-original-style="" style="box-sizing: border-box;"><li fr-original-style="" style="box-sizing: border-box;">An internet connection with a mobile/tablet or laptop is required to access the course</li><li fr-original-style="" style="box-sizing: border-box;">Access to laptop with camera is required for the exam and a pair of headphones is recommended for the exam.</li></ul></section><section style="color: rgb(15, 21, 26); box-sizing: border-box;" fr-original-style=" color: rgb(15, 21, 26);"><h2 fr-original-style="" style="box-sizing: border-box;">Career path</h2><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Globally, Artificial Intelligence solutions will be worth more than &pound;30bn by 2024 - boosting productivity up to 30% with up to 25% savings &amp; generate up to $15.7 trillion to the global economy in 2030.</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">Sectors using Artificial Intelligence:</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">&bull; Health</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">&bull; Robotics</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">&bull; Engineering &amp; Manufacturing</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">&bull; Finance</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">&bull; Marketing</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">&bull; Transport</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">&bull; Automotive</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">&bull; Advertising</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">&bull; Retail</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">&bull; Researchers</p><p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">&bull; Cyber Security</p></section></div>

Duco Digital Training

Business Analytics & Intelligence

IT Certifications

Digital Skills

This award introduces the critical concepts associated with AI and explores its relationship with the systems and processes that make up the digital ecosystem. It explores how AI can empower organisations to utilise Big Data through the use of Business Analysis and Machine Learning, and encourages candidates to consider a future vision of the world that is powered by AI.

AI and the Digital Ecosystem - BCS Foundation Award

General admission

Packt

Python

Programming

Pandas

This comprehensive course will guide you to use the power of Python to evaluate recommender system datasets based on user ratings, user choices, music genres, categories of movies, and their years of release with a practical approach to build content-based and collaborative filtering techniques for recommender systems with hands-on experience.

Recommender Systems Complete Course Beginner to Advanced

General Admission

<p>You're a UK programmer looking to upskill and advance your career. You know the demand for skilled programmers is growing daily, and you want to stay ahead. But with so many courses and programs available, it can be overwhelming to know where to start.</p><p>That's where the <strong>Pentest Programmer- QLS Endorsed Bundle </strong>comes in. This Bundle offers a wide range of courses to give you the skills and knowledge you need to succeed in today's competitive job market. And with <strong>10 QLS Endorsed Certificates included free of charge</strong>, you can be sure that a reputable and respected organisation backs your learning.</p><p>According to recent statistics, the demand for software developers in the UK alone is projected to <strong>grow by 21.2% between 2020 and 2027</strong>. And with the rise of artificial intelligence and other emerging technologies, the need for skilled programmers will only continue to increase. By investing in your aptitudes and knowledge now, you can position yourself for success and take advantage of the many opportunities in this growing field.</p><p>So why wait? Don't miss this lucrative opportunity to enhance your talents and set yourself up for success.</p><p><strong>Learning Outcomes:</strong></p><ul><li>Gain a comprehensive understanding of assembly programming and its applications across multiple platforms.</li><li>Develop mastery in C++ programming, including object-oriented programming and advanced coding techniques.</li><li>Build proficiency in HTML5 web development, including website design and multimedia integration.</li><li>Acquire expert-level knowledge of PHP and MySQL and object-oriented programming techniques.</li><li>Learn secure programming practices for web applications, including risk assessment and threat mitigation strategies.</li><li>Master Python programming fundamentals, including basic syntax, data types, and functions.</li><li>Explore the basics of machine learning with Python, including supervised and unsupervised learning algorithms and data analysis techniques.</li></ul><p><strong>Why Choose Imperial Academy?</strong></p><ul><li>Fully Online Training</li><li>QLS Endorsed &amp; CPD Accredited Certificate</li><li>No Time Limits. Study at Your Own Pace</li><li>Expert Instructors</li><li>Audio Visual Training</li><li>Up-to-date Curriculum and Valuable Guidelines</li></ul><p>Assembly Programming For All Platforms is a course designed for programmers seeking to gain expertise in assembly programming across multiple platforms, including x86, ARM, and MIPS. Through this course, students will learn how to write and debug assembly code for various applications and media and gain an understanding of the underlying principles of assembly programming.</p><p>C++ Development: The Complete Coding Guide is a course that offers a comprehensive introduction to C++ programming, taking students from beginner to advanced level. Through this course, students will develop mastery in object-oriented programming and advanced coding techniques, including data structures and algorithms. This course is designed for individuals seeking to enhance their programming skills and advance their careers in software development, web development, or related fields. This course will prepare students with the knowledge and expertise necessary to succeed in today's competitive job market.</p><p><strong>This bundle comprises the following courses:</strong></p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong>Course 01:</strong> Web Application Penetration Testing Course</li><li><strong>Course 02:</strong> Secure Programming of Web Applications - Developers and TPMs</li><li><strong>Course 03:</strong> Linux Security and Hardening</li><li><strong>Course 04:</strong> Basic HTML 5</li><li><strong>Course 05:</strong> Ultimate PHP &amp; MySQL Web Development Course &amp; OOP Coding</li><li><strong>Course 06:</strong> Dart Masterclass Programming Course: iOS/Android Bible</li><li><strong>Course 07:</strong> Machine Learning with Python</li><li><strong>Course 08:</strong> Python Programming for Non-Programmers Level 5</li><li><strong>Course 09:</strong> Career Coaching</li><li><strong>Course 10:</strong> The Unstoppable Self-Confidence &amp; High Self-Esteem Blueprint</li></ul><p><strong>Certificate: </strong></p><p>The <strong>Quality Licence Scheme</strong> ensures excellence and quality. These courses' learning outcomes also provide the learners with high competence in their line of work. So, <strong>QLS Endorsed</strong> courses deliver deep knowledge and add significant value to the resume.</p><p>After completing the assessments, learners will be eligible for a prestigious<strong> QLS Certificate </strong>for each endorsed course in this bundle. Moreover, the certificates will be delivered to their home free of cost!</p><p>Imperial Academy is an approved reselling partner for Quality Licence Scheme courses under One Education.</p><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <ul><li>Programmers looking to expand their skill set and knowledge base.</li><li>Individuals seeking to enter the field of web development or software engineering.</li><li>Professionals looking to enhance their programming skills and advance their careers.</li><li>Students pursuing a career in programming, web development, or related fields.</li><li>Anyone with an interest in learning more about programming and web development.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Anyone can enrol in the <strong>Pentest Programmer- QLS Endorsed Bundle</strong>.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Software Developer - £29,000 to £73,000 per year</li><li>Web Developer - £20,000 to £40,000 per year</li><li>Database Administrator - £25,000 to £65,000 per year</li><li>Machine Learning Engineer - £40,000 to £100,000 per year</li><li>Data Scientist - £30,000 to £90,000 per year</li><li>Cybersecurity Analyst - £25,000 to £80,000 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>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Imperial Academy

10 QLS Endorsed Courses for Pentest Programmer  | 10 Endorsed Certificates Included | Life Time Access

Pentest Programmer- QLS Endorsed Bundle

<p>Transform the future - one line of code at a time. This comprehensive online bundle equips you with the foundational knowledge and practical skills to explore the exciting world of Artificial Intelligence (AI).</p><p><strong>What you'll gain:</strong></p><ul><li><strong>15 in-depth courses</strong>: Master essential concepts, delve into real-world applications, and explore cutting-edge deep learning projects.</li><li><strong>Structured learning path</strong>: Progress from core AI principles to advanced techniques at your own pace.</li><li><strong>Interactive learning</strong>: Engage with video lectures, quizzes, and practical coding exercises.</li><li><strong>Project-based learning</strong>: Apply your newfound knowledge with hands-on projects in areas like image recognition and social media automation.</li></ul><p>This bundle is designed for anyone curious about AI, from absolute beginners to those seeking to enhance their existing knowledge. The bundle culminates in advanced Deep Learning Projects where you'll build real-world AI applications.</p><p><strong>Course list: </strong></p><p><strong>Course 01: Foundations of Artificial Intelligence:</strong> Explore the core concepts of AI, including machine learning, deep learning, and natural language processing.</p><p><strong>Course 02: Introduction to Statistics:</strong> Grasp the fundamental statistical concepts crucial for understanding and working with AI data.</p><p><strong>Course 03: PIC Microcontroller Simulation:</strong> Gain experience in microcontroller programming, a valuable skill for building physical AI systems.</p><p><strong>Course 04: Probability and Statistics Essential Training:</strong> Strengthen your understanding of probability and statistics, essential tools for analysing AI data.</p><p><strong>Course 05: Statistics &amp; Probability for Data Science &amp; Machine Learning:</strong> Delve deeper into statistical analysis specifically for data science and machine learning applications.</p><p><strong>Course 06: Project on Deep Learning - Artificial Neural Network:</strong> Build your own deep learning model using artificial neural networks.</p><p><strong>Course 07: ChatGPT &amp; AI: 23 Ways to Make Money with AI:</strong> Explore the potential of AI for generating income through various creative and entrepreneurial avenues.</p><p><strong>Course 08: Master in JavaScript Quickly Using ChatGPT Open AI:</strong> Utilise ChatGPT's capabilities to accelerate your mastery of JavaScript programming.</p><p><strong>Course 09: ChatGPT for Marketing and Productivity with AI Tools:</strong> Discover how ChatGPT and other AI tools can enhance your marketing and productivity strategies.</p><p><strong>Course 10: Data Science &amp; Machine Learning with Python:</strong> Learn Python, a popular programming language widely used in data science and machine learning.</p><p><strong>Course 11: Hands-on Machine Learning Project - Covid Mask Detector:</strong> Develop a machine learning model that detects the presence of face masks in images.</p><p><strong>Course 12: Hands-on Machine Learning Project - Auto Image Captioning for Social Media:</strong> Build an AI system that automatically generates captions for your social media images.</p><p><strong>Course 13: Deep Learning Projects - Handwritten Digit Recognition Using Neural Network:</strong> Create a deep learning model that can recognise handwritten digits.</p><p><strong>Course 14: Deep Learning Projects - Convolutional Neural Network:</strong> Explore the power of convolutional neural networks (CNNs) for image recognition tasks.</p><p><strong>Course 15: Deep Learning &amp; Neural Networks Python - Keras:</strong> Learn Keras, a popular Python library for building and training deep learning models.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        40 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 with no prior AI knowledge who want to start their AI journey.</li><li>Professionals seeking to upskill in AI and machine learning.</li><li>Anyone interested in exploring the potential of AI for creative or business applications.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This bundle has minimal entry requirements, requiring only access to a computer or mobile device with internet connectivity, basic computer literacy, proficiency in the English language, and a genuine eagerness to learn. We welcome learners from all backgrounds to join us in exploring new subjects and expanding their knowledge. </p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This bundle opens doors to a range of exciting careers in the booming field of AI, including:</p><ul><li>Machine Learning Engineer</li><li>Data Scientist</li><li>AI Developer</li><li>AI Researcher</li><li>Robotics Engineer</li></ul><p>And many more!</p>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4> Digital certificate</h4>
        <p>
            Digital certificate -
            £9
        </p>
        <h4> Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div><p>A Hardcopy Certificate (international): £15 + £10 (postal charge) = £25</p></div>

                        </div>

One Education

Artificial Intelligence (Online) Course

<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 />Developers responsible for developing Deep Learning applications<br />Developers who want to understand concepts behind Deep Learning and how to implement a Deep Learning solution on AWS</p>Overview<p>This course is designed to teach you how to:<br />Define machine learning (ML) and deep learning<br />Identify the concepts in a deep learning ecosystem<br />Use Amazon SageMaker and the MXNet programming framework for deep learning workloads<br />Fit AWS solutions for deep learning deployments</p><p>In this course, you?ll learn about AWS?s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You?ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You?ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS.</p></div><div id="course-pills-2"><h4>Module 1: Machine learning overview </h4><ul><li>A brief history of AI, ML, and DL</li><li>The business importance of ML</li><li>Common challenges in ML</li><li>Different types of ML problems and tasks</li><li>AI on AWS</li></ul><h4>Module 2: Introduction to deep learning </h4><ul><li>Introduction to DL </li><li>The DL concepts</li><li>A summary of how to train DL models on AWS</li><li>Introduction to Amazon SageMaker</li><li>Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model</li></ul><h4> Module 3: Introduction to Apache MXNet </h4><ul><li>The motivation for and benefits of using MXNet and Gluon</li><li>Important terms and APIs used in MXNet</li><li>Convolutional neural networks (CNN) architecture </li><li>Hands-on lab: Training a CNN on a CIFAR-10 dataset</li></ul><h4>Module 4: ML and DL architectures on AWS </h4><ul><li>AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk)</li><li>Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition)</li><li>Hands-on lab: Deploying a trained model for prediction on AWS Lambda</li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Deep Learning on AWS 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 Deep Learning on AWS 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

Learning & Development

Managed Learning Programme (MLP)

Trainers Course

Azure Machine Learning

AWS (Amazon Web Services)

Deep Learning

Deep Learning on AWS

<strong>This course will help you prepare for the AI-900 Exam: Microsoft Azure AI Fundamentals. We will cover the complete exam syllabus as updated in April 2021 with sample questions.</strong>
        <p>Prepare for the AI-900 Certification Exam by covering the complete exam syllabus as updated in April 2021 with sample questions with this course.

We will be covering the following 5 domains in the course:

Domain 1: We will cover the identification of features in common AI workloads and guiding principles for responsible AI.

Domain 2: We will cover the identification of common machine learning variants, description of core machine learning concepts, identification of core risks in the creation of a machine learning solution, and description of capabilities of no-code machine learning with Azure machine learning.

Domain 3: We will cover the identification of common types of computer vision solutions and the identification of Azure tools and services for computer vision tasks.

Domain 4: We will cover the identification of features in common NLP workload scenarios and learn how to identify Azure tools and services for NLP workloads.

Domain 5: We will cover the identification of common use cases for conversational AI and will be identifying Azure services for conversational AI.

By the end of this course, you will be ready to appear for your AI-900 exam.

All the resources for this course are available at https://github.com/PacktPublishing/AI-900-Microsoft-Azure-AI-Fundamentals-Video-Course-and-Ques.</p>
        <h2>What You Will Learn</h2><p>Learn foundational knowledge of machine learning and artificial intelligence<br>Learn common ML and AI workloads and how to implement them on Azure<br>Cover principles of machine learning on Azure<br>Learn about computer vision workloads on Azure<br>Cover Natural Language Processing (NLP) workloads on Azure<br>Understand conversational AI workloads on Azure</p>
        <h2>Audience</h2><p>This course is designed for those who are willing to give AI-900 Exam: Microsoft Azure AI Fundamentals and are interested in learning the basics of artificial intelligence or machine learning.<br><br>No prerequisite is required for this course. Candidates with both technical and non-technical backgrounds can benefit from this course.</p>
        <h2>Approach</h2><p>In this course, we will prepare for the AI-900 exam by following the exam syllabus and covering all five domains one by one. The course also includes sample questions that will help you track your learning process.</p>
        <h2>Key Features</h2><p>Cover 100% of the syllabus as updated in April 2021 * Prepare for AI-900 Microsoft Azure AI Fundamentals certification exam * Solve sample questions to track your learning progress</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/AI-900-Microsoft-Azure-AI-Fundamentals-Video-Course-and-Ques</p>
        <h2>About the Author</h2><h3>Eshant Garg</h3><p>Eshant Garg has 16 years of extensive professional experience with expertise in database and business intelligence solutions, advanced analytics, design and solution architecture, reporting, and cloud computing technologies (Azure and AWS). He loves to explain complicated things in a simple and effective way. As a developer and architect, he has worked closely with customers, users, and colleagues to support business solutions across a variety of industries including healthcare, insurance, finance, and government ranging from small companies to Fortune 500 companies.

Outside of the technical world, he loves yoga and meditation. He is a student of the ancient yogic text, the Bhagavad Gita, and loves to discuss and practice philosophical teachings.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p>Welcome to the course! Let's take a quick introduction to the course.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Introduction</p><p style='margin: 0'>In this video, we will cover course introduction.</p></td></tr></tbody></table><br><h4>2. Azure Portal Introduction: For Beginners</h4><p>In this section, we will cover a quick Azure portal introduction.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Create Azure Free Subscription</p><p style='margin: 0'>In this video, you will learn how to create Azure free subscription.</p></td></tr><tr><td style='padding: 15px;'><p>2. Azure Portal Overview</p><p style='margin: 0'>In this video, we will cover Azure portal overview.</p></td></tr><tr><td style='padding: 15px;'><p>3. Azure Sandbox - How to Use Azure Portal Absolutely Free</p><p style='margin: 0'>In this video, we will cover Azure Sandbox - how to use Azure Portal absolutely free.</p></td></tr></tbody></table><br><h4>3. AI Workloads and Considerations (15-20%)</h4><p>In this section, we will cover domain 1 - AI workloads and considerations, which covers 15-20% of the exam.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives</p><p style='margin: 0'>In this video, we will understand the learning objectives of this domain.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Artificial Intelligence</p><p style='margin: 0'>In this video, we will cover what artificial intelligence is.</p></td></tr><tr><td style='padding: 15px;'><p>3. Prediction and Forecasting</p><p style='margin: 0'>In this video, we will cover prediction and forecasting.</p></td></tr><tr><td style='padding: 15px;'><p>4. Anomaly Detection Workloads</p><p style='margin: 0'>In this video, we will cover anomaly detection workloads.</p></td></tr><tr><td style='padding: 15px;'><p>5. Computer Vision Workloads</p><p style='margin: 0'>In this video, we will cover computer vision workloads.</p></td></tr><tr><td style='padding: 15px;'><p>6. Natural Language Processing</p><p style='margin: 0'>In this video, we will cover natural language processing.</p></td></tr><tr><td style='padding: 15px;'><p>7. Knowledge Mining Workloads</p><p style='margin: 0'>In this video, we will cover knowledge mining workloads.</p></td></tr><tr><td style='padding: 15px;'><p>8. Conversational AI Workloads</p><p style='margin: 0'>In this video, we will cover conversational AI workloads.</p></td></tr><tr><td style='padding: 15px;'><p>9. Introduction to Guiding Principles of Responsible AI</p><p style='margin: 0'>In this video, we will cover an introduction to guiding principles of responsible AI.</p></td></tr><tr><td style='padding: 15px;'><p>10. Guiding Principle - Fairness</p><p style='margin: 0'>In this video, we will cover a guiding principle - fairness.</p></td></tr><tr><td style='padding: 15px;'><p>11. Guiding Principle - Reliability and Safety</p><p style='margin: 0'>In this video, we will cover a guiding principle - reliability and safety.</p></td></tr><tr><td style='padding: 15px;'><p>12. Guiding Principle - Privacy and Security</p><p style='margin: 0'>In this video, we will cover a guiding principle - privacy and security.</p></td></tr><tr><td style='padding: 15px;'><p>13. Guiding Principle - Inclusiveness</p><p style='margin: 0'>In this video, we will cover a guiding principle - inclusiveness.</p></td></tr><tr><td style='padding: 15px;'><p>14. Guiding Principle - Transparency</p><p style='margin: 0'>In this video, we will cover a guiding principle - transparency.</p></td></tr><tr><td style='padding: 15px;'><p>15. Guiding Principle - Accountability</p><p style='margin: 0'>In this video, we will cover a guiding principle - accountability.</p></td></tr></tbody></table><br><h4>4. Fundamental Principles of Machine Learning on Azure (30- 35%)</h4><p>In this section, we will cover domain 2 - fundamental principles of machine learning on Azure, which covers 30- 35% of the exam.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives</p><p style='margin: 0'>In this video, we will understand the learning objectives of this domain.</p></td></tr><tr><td style='padding: 15px;'><p>2. Introduction to Machine Learning</p><p style='margin: 0'>In this video, we will cover an introduction to machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>3. Rule-Based Versus Machine Learning Based Learning</p><p style='margin: 0'>In this video, we will cover rule-based versus machine learning based learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Classification Versus Regression Versus Clustering Machine Learning Types</p><p style='margin: 0'>In this video, we will cover classification versus regression versus clustering machine learning types.</p></td></tr><tr><td style='padding: 15px;'><p>5. Feature Selection and Feature Engineering</p><p style='margin: 0'>In this video, we will cover feature selection and feature engineering.</p></td></tr><tr><td style='padding: 15px;'><p>6. Training Versus Validating Dataset</p><p style='margin: 0'>In this video, we will cover training versus validating dataset.</p></td></tr><tr><td style='padding: 15px;'><p>7. Machine Learning Algorithms</p><p style='margin: 0'>In this video, we will cover machine learning algorithms.</p></td></tr><tr><td style='padding: 15px;'><p>8. Demo Part1.1 ML Workspace</p><p style='margin: 0'>In this demo video, we will work on ML workspace.</p></td></tr><tr><td style='padding: 15px;'><p>9. Demo Part1.2 Regression Model</p><p style='margin: 0'>In this demo video, we will work on regression model.</p></td></tr><tr><td style='padding: 15px;'><p>10. Demo Part1.3 Delete Resources</p><p style='margin: 0'>In this demo video, we will work on deleting resources.</p></td></tr><tr><td style='padding: 15px;'><p>11. Demo 2.1 Classification Model</p><p style='margin: 0'>In this demo video, we will work on classification model.</p></td></tr><tr><td style='padding: 15px;'><p>12. Demo 3.1 Automated Machine Learning</p><p style='margin: 0'>In this demo video, we will work on automated machine learning.</p></td></tr><tr><td style='padding: 15px;'><p>13. Demo: Delete Compute</p><p style='margin: 0'>In this demo video, we will work on delete compute.</p></td></tr></tbody></table><br><h4>5. Describe Features of Computer Vision Workloads on Azure (15-20%)</h4><p>In this section, we will cover domain 3 - describe features of computer vision workloads on Azure, which covers 15-20% of the exam.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives</p><p style='margin: 0'>In this video, we will understand the learning objectives of this domain.</p></td></tr><tr><td style='padding: 15px;'><p>2. Image Classification Versus Object Detection Versus Semantic Segmentation</p><p style='margin: 0'>In this video, we will cover image classification versus object detection versus semantic segmentation.</p></td></tr><tr><td style='padding: 15px;'><p>3. Optical Character Recognition (OCR)</p><p style='margin: 0'>In this video, we will cover Optical Character Recognition (OCR).</p></td></tr><tr><td style='padding: 15px;'><p>4. Face Detection Recognition and Analysis</p><p style='margin: 0'>In this video, we will cover face detection recognition and analysis.</p></td></tr><tr><td style='padding: 15px;'><p>5. What are Cognitive Services</p><p style='margin: 0'>In this video, we will cover cognitive services.</p></td></tr><tr><td style='padding: 15px;'><p>6. What are Computer Vision Services</p><p style='margin: 0'>In this video, we will cover computer vision services.</p></td></tr><tr><td style='padding: 15px;'><p>7. Demo: Computer Vision</p><p style='margin: 0'>In this demo video, we will work on computer vision.</p></td></tr><tr><td style='padding: 15px;'><p>8. Custom Vision Service</p><p style='margin: 0'>In this video, we will cover custom vision service.</p></td></tr><tr><td style='padding: 15px;'><p>9. Demo: Custom Vision Service</p><p style='margin: 0'>In this demo video, we will work on custom vision service.</p></td></tr><tr><td style='padding: 15px;'><p>10. Face Service</p><p style='margin: 0'>In this video, we will cover face service.</p></td></tr><tr><td style='padding: 15px;'><p>11. Form Recognizer Service</p><p style='margin: 0'>In this video, we will cover form recognizer service.</p></td></tr></tbody></table><br><h4>6. Natural Language Processing (NLP) Workloads on Azure (15-20%)</h4><p>In this section, we will cover domain 4 - Natural Language Processing (NLP) workloads on Azure, which covers 15-20% of the exam.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives</p><p style='margin: 0'>In this video, we will understand the learning objectives of this domain.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Natural Language Processing</p><p style='margin: 0'>In this video, we will cover Natural Language Processing.</p></td></tr><tr><td style='padding: 15px;'><p>3. Key Phrase Extraction Versus Entity Recognition Versus Sentiment Analysis</p><p style='margin: 0'>In this video, we will cover key phrase extraction versus entity recognition versus sentiment analysis.</p></td></tr><tr><td style='padding: 15px;'><p>4. Language Modelling</p><p style='margin: 0'>In this video, we will cover language modelling.</p></td></tr><tr><td style='padding: 15px;'><p>5. Speech Recognition and Speech Synthesis</p><p style='margin: 0'>In this video, we will cover speech recognition and speech synthesis.</p></td></tr><tr><td style='padding: 15px;'><p>6. Translation</p><p style='margin: 0'>In this video, we will cover translation.</p></td></tr><tr><td style='padding: 15px;'><p>7. Introduction to Azure Tools and Services for NLP</p><p style='margin: 0'>In this video, we will cover an introduction to Azure tools and services for NLP.</p></td></tr><tr><td style='padding: 15px;'><p>8. Text Analytics Service</p><p style='margin: 0'>In this video, we will cover text analytics service.</p></td></tr><tr><td style='padding: 15px;'><p>9. Speech Service</p><p style='margin: 0'>In this video, we will cover speech service.</p></td></tr><tr><td style='padding: 15px;'><p>10. Translator Service</p><p style='margin: 0'>In this video, we will cover translator service.</p></td></tr><tr><td style='padding: 15px;'><p>11. Language Understanding Service (LUIS)</p><p style='margin: 0'>In this video, we will cover Language Understanding Service (LUIS).</p></td></tr></tbody></table><br><h4>7. Conversational AI Workloads on Azure (15-20%)</h4><p>In this section, we will cover domain 5 - conversational AI workloads on Azure, which covers 15-20% of the exam.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Learning Objectives</p><p style='margin: 0'>In this video, we will understand the learning objectives of this domain.</p></td></tr><tr><td style='padding: 15px;'><p>2. Conversational AI Use Cases</p><p style='margin: 0'>In this video, we will cover conversational AI use cases.</p></td></tr><tr><td style='padding: 15px;'><p>3. QnA Maker and Bot Framework</p><p style='margin: 0'>In this video, we will cover QnA Maker and Bot framework.</p></td></tr><tr><td style='padding: 15px;'><p>4. Demo QnA Maker and Bot Framework</p><p style='margin: 0'>In this demo video, we will work on QnA Maker and Bot framework.</p></td></tr></tbody></table><br><h4>8. Practice Tests</h4><p>In this section, we will understand the exam structure.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. AI-900 Exam Tips</p><p style='margin: 0'>In this video, we will cover AI-900 Exam tips.</p></td></tr></tbody></table>

Microsoft

This course will help you prepare for the AI-900 Exam: Microsoft Azure AI Fundamentals. We will cover the complete exam syllabus as updated in April 2021 with sample questions.

AI-900: Microsoft Azure AI Fundamentals Video Course + Ques

<p>Data scientists are among the most sought-after professionals in today's job market. According to recent statistics, the demand for skilled data scientists has increased by a staggering 40% in the past year alone. So, seize this opportunity with our comprehensive "Data Science Masterclass" bundle, meticulously crafted to propel you into the heart of the data-driven revolution.</p><p>Kickstart your journey with the foundational "Quick Data Science Approach from Scratch" and "Data Science &amp; Machine Learning with Python" courses, equipping you with essential skills from the get-go. As you progress through the carefully designed curriculum, you will get to explore the intricacies of statistics, probability, R programming, MySQL, and SQL - each a stepping stone towards mastering the art of data science. These courses are not just building blocks but the cornerstone of your future success in the data science industry.</p><p>Embark on a transformative learning experience, gaining proficiency in Python, JavaScript, Microsoft SQL, MySQL Database Development, and Data Analysis in Excel. The finale, "Data Protection," ensures you understand the ethical and legal aspects of handling data. Elevate your career prospects and become an indispensable asset in any data-centric domain.</p><p>Don't let this opportunity pass you by; enrol in the bundle right now and sculpt your path to becoming a proficient data scientist!</p><p><strong>What will you learn from this CPDQS Accredited Data Science Masterclass Bundle?</strong></p><p>By the end of this bundle, you should be able to:</p><ul><li>Apply Python for data manipulation, analysis, and machine learning.</li><li>Master statistical concepts crucial for data science and machine learning.</li><li>Harness R programming for effective data analysis and visualization.</li><li>Build and manage MySQL databases for data storage and retrieval.</li><li>Integrate Python, JavaScript, and Microsoft SQL in data science projects.</li><li>Demonstrate proficiency in data analysis using Excel tools.</li></ul><p>This comprehensive "Data Science Masterclass" bundle covers a spectrum of essential skills. Begin with foundational data science principles and Python programming. Progress to mastering statistical concepts vital for data analysis and machine learning. Learn R programming for effective data manipulation and visualisation. Explore database development with MySQL and delve into integrating Python, JavaScript, and Microsoft SQL. Cap it off with advanced data analysis in Excel and a focus on ethical data protection practices. This bundle ensures a holistic and practical approach to becoming a proficient data scientist.</p><p>This Data Science Masterclass bundle consists of the following 11 courses:</p><ol><li>Quick Data Science Approach from Scratch</li><li>Data Science &amp; Machine Learning with Python</li><li>Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li>R Programming for Data Science</li><li>Learn MySQL from Scratch for Data Science and Analytics</li><li>Learn Python, JavaScript, and Microsoft SQL for Data science</li><li>SQL for Data Science, Data Analytics and Data Visualization</li><li>Python Data Science</li><li>MySQL Database Development</li><li>Data Analysis In Excel</li><li>Data Protection</li></ol><p><strong>Certification</strong><br />Once you have finished the course, you will be eligible to apply for a CPD QS accredited certificate as proof of your new skills and your continued professional development.<br />The learner will get FREE PDF Certificate for the Quick Data Science Approach from Scratch course.<br />For the rest of the courses the following fee structure will be applicable.</p><ul><li>CPD QS Accredited PDF Certificate: £9</li><li>CPD QS Accredited Hardcopy Certificate: £15 (Additional £10 will be charged for International Students).</li></ul><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        35 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>Aspiring Analysts</li><li>Tech Enthusiasts</li><li>Excel Users</li><li>Programming Beginners</li><li>Data Curious Individuals</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This bundle is open to all and requires no formal prerequisites. Dive into the "Data Science Masterclass" with ease, as it welcomes enthusiasts, beginners, and professionals alike. No prior experience is necessary, ensuring accessibility for anyone eager to embark on a transformative journey through the realms of data science, programming, statistics, and database management. Join without hesitation and elevate your skills in this comprehensive and inclusive learning experience.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Analyst: £35,000 - £45,000</li><li>Database Administrator: £40,000 - £50,000</li><li>Machine Learning Engineer: £45,000 - £60,000</li><li>Business Intelligence Analyst: £35,000 - £50,000</li><li>Data Scientist: £40,000 - £55,000</li><li>Analytics Consultant: £45,000 - £60,000</li></ul>
                        </div>

Data Science Masterclass Course

<p><strong>Transform Your Career with Our IT: Data Analysis, Data Science &amp; Data Protection Program - an all-in-one Program Designed for Mastery!</strong></p><p>Do you know the demand for IT professionals with expertise in data science is skyrocketing? This Ultimate IT: Data Analysis, Data Science &amp; Data Protection Program is your gateway to a thriving career in this dynamic industry. This program is meticulously designed to equip you with the knowledge and skills demanded by hiring managers across various sectors. By enrolling in this IT: Data Analysis, Data Science &amp; Data Protection program, you'll embark on a journey that opens doors to exciting opportunities and empowers you to shape your future in the IT industry.</p><p>Our IT: Data Analysis, Data Science &amp; Data Protection program will give you a comprehensive understanding of data analysis, from data collection and preparation to data visualisation and communication. 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We're so confident with our program that we offer a <strong>100% money-back guarantee</strong>, ensuring your complete satisfaction.</p><p><strong>Learning Outcomes</strong></p><p>By completing this IT: Data Analysis, Data Science &amp; Data Protection program, you will gain expertise in the following:</p><ul><li>Data analysis techniques and methodologies.</li><li>Python programming for data analysis.</li><li>Business intelligence and data mining.</li><li>Advanced Excel techniques, including VBA and Power Query.</li><li>SQL programming and big data technologies.</li><li>Data Science &amp; Data Protection, Machine Learning with Python and R.</li><li>Data visualisation with tools like Tableau and Power BI.</li><li>Statistics and probability for data science.</li><li>Effective career development and job-seeking skills.</li><li>Design an engaging resume and excel in the job search.</li><li>Succeed in interviews, including video interviews.</li><li>Build a strong LinkedIn profile to connect with professionals and enhance your online visibility in IT: Data Analysis- Data Science &amp; Data Protection field.</li></ul><p><strong>Courses Included in the Program</strong></p><p>You get <strong>25 in-demand courses</strong> once you enrol in our IT: Data Analysis, Data Science &amp; Data Protection program.</p><ul><li><strong>=&gt; Course 01:</strong> Introduction to Data Analysis</li><li><strong>=&gt; Course 02:</strong> Data Analytics</li><li><strong>=&gt; Course 03:</strong> Python for Data Analysis</li><li><strong>=&gt; Course 04:</strong> Basic Google Data Studio</li><li><strong>=&gt; Course 05:</strong> Business Intelligence and Data Mining Masterclass</li><li><strong>=&gt; Course 06:</strong> Microsoft Excel: Automated Dashboard Using Advanced Formula, VBA, Power Query</li><li><strong>=&gt; Course 07:</strong> SQL Programming Masterclass</li><li><strong>=&gt; Course 08:</strong> SQL NoSQL Big Data and Hadoop</li><li><strong>=&gt; Course 09:</strong> Data Science &amp; Machine Learning with Python</li><li><strong>=&gt; Course 10:</strong> Machine Learning with Python</li><li><strong>=&gt; Course 11:</strong> Data Science &amp; Machine Learning with R</li><li><strong>=&gt; Course 12:</strong> Data Analytics with Tableau</li><li><strong>=&gt; Course 13:</strong> Develop Big Data Pipelines with R &amp; Sparklyr &amp; Tableau</li><li><strong>=&gt; Course 14:</strong> Complete Introduction to Business Data Analysis Level 3</li><li><strong>=&gt; Course 15:</strong> Data Analysis in Microsoft Excel Complete Training</li><li><strong>=&gt; Course 16:</strong> Excel Data Analysis for Beginner</li><li><strong>=&gt; Course 17:</strong> GDPR Data Protection Level 5</li><li><strong>=&gt; Course 18: </strong>Master JavaScript with Data Visualization</li><li><strong>=&gt; Course 19:</strong> Data Visualization and Reporting with Power BI</li><li><strong>=&gt; Course 20:</strong> Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong>=&gt; Course 21:</strong> Career Development Plan Fundamentals</li><li><strong>=&gt; Course 22:</strong> CV Writing and Job Searching</li><li><strong>=&gt; Course 23:</strong> Interview Skills: Ace the Interview</li><li><strong>=&gt; Course 24:</strong> Video Job Interview for Job Seekers</li><li><strong>=&gt; Course 25:</strong> How to Create a Professional LinkedIn Profile</li></ul><p>Enrol in our highly regarded IT: Data Analysis, Data Science &amp; Data Protection program, featuring a <strong>job-relevant curriculum</strong> that ensures your skills <strong>align with employer expectations</strong> across various sectors. Don't miss this opportunity - your success story starts now!</p><p>Our IT: Data Analysis, Data Science &amp; Data Protection Program is a comprehensive and industry-relevant journey through data analysis, data science, and IT analytics. With a focus on providing theoretical knowledge and academic depth, this program is your gateway to a promising career in IT: Data Analysis, Data Science &amp; Data Protection sector.</p><p><strong>Why Choose Us?</strong></p><p>We take great pride in offering you a great learning experience that stands out. When you consider enrolling in our IT: Data Analysis, Data Science &amp; Data Protection program, you're making a decision that will positively impact your career and knowledge in various aspects related to IT: Data Analysis, Data Science &amp; Data Protection. Here's why choosing us is a smart choice:</p><p><strong>Updated Materials: </strong>We're committed to providing the most up-to-date learning materials. Our dedicated team continuously reviews and updates our content, ensuring you're always learning from the latest sources. When you choose us, you select the most current and relevant information, giving you the edge in your IT career.</p><p><strong>Flexible Timing: </strong>We understand that life can get busy, and you may have existing commitments that can make pursuing further education challenging. That's why we offer flexibility in your study schedule. With our courses, you can learn at your own pace, on your terms. You're in control and can adjust your learning to fit your life.</p><p><strong>No Hidden Cost: </strong>When choosing our program, you won't incur additional expenses. The certification and course materials are all-inclusive within the program's price. You can focus on your studies without worrying about hidden fees.</p><p><strong>Money-Back Guarantee: </strong>Your satisfaction is our top priority. We're so confident in the quality of our courses that we back them up with a <strong>14-day </strong>money-back guarantee. We'll refund your investment if you're unsatisfied with your learning experience.</p><p><strong>Lifetime Access: </strong>When you choose to learn with us, you gain access to a course and a lifetime of knowledge. We offer lifetime access to our course materials, allowing you to revisit and refresh your knowledge whenever you need.</p><p><strong>24/7 Support: </strong>Learning doesn't just happen during traditional working hours; neither should support. Our commitment to your success extends beyond the classroom. We provide 24/7 support, so you can contact us with your questions and concerns anytime.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>This IT: Data Analysis, Data Science &amp; Data Protection program is suitable for:</p><ul><li>Aspiring IT: Data Analysis, Data Science &amp; Data Protection professionals.</li><li>Students and recent graduates looking to enter the field.</li><li>Career changers interested in data analytics.</li><li>Security professionals seeking to upskill in data security.</li><li>Anyone interested in learning about IT: Data Analysis, Data Science &amp; Data Protection.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No prior experience is required in our IT: Data Analysis, Data Science &amp; Data Protection program.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Upon completing the program, you'll get edges in various IT: Data Analysis, data science &amp; data protection-related jobs including:</p><ul><li><strong>Data Analyst:</strong> £25,000 - £45,000</li><li><strong>Business Intelligence Analyst:</strong> £30,000 - £50,000</li><li><strong>Data Scientist:</strong> £35,000 - £60,000</li><li><strong>Machine Learning Engineer:</strong> £40,000 - £70,000</li><li><strong>SQL Developer:</strong> £30,000 - £55,000</li><li><strong>Tableau Developer:</strong> £35,000 - £60,000</li><li><strong>Power BI Developer:</strong> £35,000 - £60,000</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited (e-Certificate)</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited (Hard Copy Certificate)</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>e-Transcript</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Hard Copy Transcript</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
        <h4>Student ID Card</h4>
        <p>
            Digital certificate -
            Included
        </p>

                        </div>

Apex Learning

Master IT: Data Analysis, Data Science & Data Protection Career Based Job Focused Program

<p><strong> 24-Hour Knowledge Knockdown! Prices Reduced Like Never Before </strong></p><p>Are you fascinated by the potential of AI to revolutionise our world? The UK government has pledged<strong> £1 billion to invest in AI research</strong>, creating exciting job opportunities in this dynamic field. If you're curious about how AI works and want to explore its practical applications, this <strong>Artificial Intelligence, Open AI &amp; ChatGPT bundle</strong> is for you!</p><p><strong>With this Artificial Intelligence, Open AI &amp; ChatGPT course you will get 20 CPD Accredited PDF Certificates, Hard Copy Certificate of Project on Deep Learning - Artificial Neural Network and our exclusive student ID card absolutely free.</strong><strong><br /></strong></p><p><strong>Courses Are Included In This Artificial Intelligence, Open AI &amp; ChatGPT Bundle:</strong><strong><br /></strong></p><ul><li><strong> Course 01: </strong>Project on Deep Learning - Artificial Neural Network</li><li><strong> Course 02: </strong>Deep Learning &amp; Neural Networks Python - Keras</li><li><strong> Course 03: </strong>Machine Learning Basics</li><li><strong> Course 04: </strong>Hands on Machine Learning Project - Auto Image Captioning for Social Media</li><li><strong> Course 05: </strong>Statistics &amp; Probability for Data Science &amp; Machine Learning</li><li><strong> Course 06: </strong>Hands on Deep Learning Projects - Stock price Prognostics</li><li><strong> Course 07: </strong>Deep Learning Projects - Convolutional Neural Network</li><li><strong> Course 08: </strong>Deep Learning Projects - Handwritten Digit Recognition Using Neural Network</li><li><strong> Course 09: </strong>Essential Training on Deep Learning Heuristic using R</li><li><strong> Course 10: </strong>Project on Deep Learning - Artificial Neural Network</li><li><strong> Course 11: </strong>Machine Learning with Python Course</li><li><strong> Course 12: </strong>Python Data Science</li><li><strong> Course 13: </strong>Quick Data Science Approach from Scratch</li><li><strong> Course 14: </strong>Data Science &amp; Machine Learning with R Training</li><li><strong> Course 15: </strong>Azure Machine Learning</li><li><strong> Course 16: </strong>A Beginner Crash Course on ChatGPT</li><li><strong> Course 17: </strong>ChatGPT Complete Guide with Expertise</li><li><strong> Course 18: </strong>ChatGPT Masterclass: A Complete ChatGPT Zero to Hero!</li><li><strong> Course 19: </strong>ChatGPT for Marketing and Productivity with AI Tools</li><li><strong> Course 20: </strong>Fundamentals to Identifying Market Trends You Can Capitalise On</li></ul><p>This comprehensive Artificial Intelligence, Open AI &amp; ChatGPT bundle equips you with the essential skills to thrive in the AI revolution. You'll learn about deep learning, building powerful neural networks that can learn and adapt like the human brain. Master Python, the language of Artificial Intelligence, and explore practical applications like image captioning and stock price prediction.</p><p>Don't miss this chance to elevate your career and skillset. Enrol in Artificial Intelligence, Open AI &amp; ChatGPT today and take the first step towards achieving your goals and dreams.</p><p><strong>Why buy this Artificial Intelligence, Open AI &amp; ChatGPT?</strong></p><ul><li><strong>Free CPD Accredited Certificate</strong> upon completion of Artificial Intelligence, Open AI &amp; ChatGPT</li><li>Get <strong>a free student ID card</strong> with Artificial Intelligence, Open AI &amp; ChatGPT</li><li><strong>Lifetime access</strong> to the Artificial Intelligence, Open AI &amp; ChatGPT course materials</li><li>Get instant access to this Artificial Intelligence, Open AI &amp; ChatGPT course</li><li>Learn Artificial Intelligence, Open AI &amp; ChatGPT from anywhere in the world</li><li>24/7 tutor support with the Artificial Intelligence, Open AI &amp; ChatGPT course.</li><li>Artificial Intelligence, Open AI &amp; ChatGPT is an entirely online, interactive lesson with voiceover audio.</li></ul> <p><strong>Start your learning journey straightaway with our Artificial Intelligence, Open AI &amp; ChatGPT Training!</strong></p><p>This Artificial Intelligence's curriculum has been designed by Artificial Intelligence experts with years of Artificial Intelligence experience behind them. The Artificial Intelligence course is extremely dynamic and well-paced to help you understand Artificial Intelligence with ease. You'll discover how to master the Artificial Intelligence skill while exploring relevant and essential topics.</p><p><strong>Certification of Artificial Intelligence, Open AI &amp; ChatGPT</strong></p><p>You have to complete the assignment given at the end of the Artificial Intelligence, Open AI &amp; ChatGPT course and score a minimum of 60% to pass each exam. Our expert trainers will assess your assignment and give you feedback after you submit the assignment. After passing the Artificial Intelligence, Open AI &amp; ChatGPT exam You will be entitled to claim a PDF &amp; Hardcopy certificate accredited by CPD Quality standards completely free.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        200 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 Artificial Intelligence, Open AI &amp; ChatGPT course is ideal for:</p><ul><li>Students seeking mastery in Artificial Intelligence, Open AI &amp; ChatGPT</li><li>Professionals seeking to enhance Artificial Intelligence, Open AI &amp; ChatGPT skills</li><li>Individuals looking for a Artificial Intelligence, Open AI &amp; ChatGPT-related career.</li><li>Anyone passionate about Artificial Intelligence, Open AI &amp; ChatGPT</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Artificial Intelligence, Open AI &amp; ChatGPT doesn't require prior experience and is suitable for diverse learners.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This Artificial Intelligence, Open AI &amp; ChatGPT bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited  Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>CPD Accredited  Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>If you are an <strong>international student</strong>, then you have to pay an additional <strong>10 GBP</strong> for each certificate as an international delivery charge.</p></div>

                        </div>

Academy for Health and Fitness

Online Course Marketing

NLP Practitioner

Microsoft AI-900

AI (Artificial Intelligence)

Artificial Intelligence, Open AI & ChatGPT

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<section>
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<h2>About this training course</h2>
<p>Gas-lift is one of the predominant forms of artificial lift used for lifting liquids from conventional, unconventional, onshore and offshore assets. Gas-lift and its various forms (intermittent lift, gas-assisted plunger lift) allows life of well lift-possibilities when selected and applied properly. This 5-day training course is designed to give participants a thorough understanding of gas-lift technology and related application concepts.</p>
<p>This training course covers main components such as application envelope, relative strengths and weaknesses of gas-lift and its different forms like intermittent lift, gas-assisted plunger lift. Participants solve examples and class problems throughout the course. Animations and videos reinforce the concepts under discussion.</p>
<p><strong>Unique Features:</strong></p>
<ul>
<li>Hands-on usage of <strong>SNAP Software</strong> to solve gas-lift exercises</li>
<li>Discussion on <strong>digital oil field</strong></li>
<li><strong>Machine learning</strong> <strong>applications</strong> in gas-lift optimization</li>
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Training Objectives 

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<p>After the completion of this training course, participants will be able to:</p>
<ul>
<li>Understand the fundamental theories and procedures related to Gas-Lift operations</li>
<li>Easily recognize the different components of the gas-lift system and their basic structural and operational features</li>
<li>Be able to design a gas-lift installation</li>
<li>Comprehend how digital oilfield tools help address ESP challenges</li>
<li>Examine recent advances in real-time approaches to the production monitoring and lift management</li>
</ul>
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Target Audience 

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<p>This training course is suitable and will greatly benefit the following specific groups:</p>
<ul>
<li>Production, reservoir, completion, drilling and facilities engineers, analysts, and operators</li>
<li>Anyone interested in learning about implications of gas-lift systems for their fields and reservoirs</li>
</ul>
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Course Level 

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<ul>
<li>Intermediate</li>
<li>Advanced</li>
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Training Methods 

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<p><span>The training instructor relies on a highly interactive training method to enhance the learning process. This method ensures that all participants gain a complete understanding of all the topics covered. The training environment is highly stimulating, challenging, and effective because the participants will learn by case studies which will allow them to apply the material taught in their own organization.</span></p>
<p><em><span>Course Duration: 5 days in total (35 hours).</span></em></p>
<p><strong><span>Training Schedule</span></strong></p>
<p><span>0830 - Registration </span><span>0900 - Start of training </span><span>1030 - Morning Break </span><span>1045 - Training recommences </span><span>1230 - Lunch Break </span><span>1330 - Training recommences </span><span>1515 - Evening break </span><span>1530 - Training recommences </span><span>1700 - End of Training</span></p>
<p><span>The maximum number of participants allowed for this training course is 20.</span></p>
<p>This course is also available through our Virtual Instructor Led Training (VILT) format.</p>
<p><strong>Prerequisites:</strong></p>
<ul>
<li>Understanding of petroleum production concepts.</li>
<li>Each participant needs a laptop/PC for solving class examples using software to be provided during class. Laptop/PC needs to have a current Windows operating system and at least 500 MB free disk space. Participants should have administrator rights to install software.</li>
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Trainer

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<p>Your expert course leader has over 35 years' work-experience in multiphase flow, artificial lift, real-time production optimization and software development/management. His current work is focused on a variety of use cases like failure prediction, virtual flow rate determination, wellhead integrity surveillance, corrosion, equipment maintenance, DTS/DAS interpretation. He has worked for national oil companies, majors, independents, and service providers globally. He has multiple patents and has delivered a multitude of industry presentations. Twice selected as an SPE distinguished lecturer, he also volunteers on SPE committees. He holds a Bachelor's and Master's in chemical engineering from the Gujarat University and IIT-Kanpur, India; and a Ph.D. in Petroleum Engineering from the University of Tulsa, USA.</p>
<p><strong>Highlighted Work Experience: </strong></p>
<ul>
<li>At Weatherford, consulted with clients as well as directed teams on digital oilfield solutions including LOWIS - a solution that was underneath the production operations of Chevron and Occidental Petroleum across the globe.</li>
<li>Worked with and consulted on equipment's like field controllers, VSDs, downhole permanent gauges, multiphase flow meters, fibre optics-based measurements.</li>
<li>Shepherded an enterprise-class solution that is being deployed at a major oil and gas producer for production management including artificial lift optimization using real time data and deep-learning data analytics.</li>
<li>Developed a workshop on digital oilfield approaches for production engineers.</li>
</ul>
<p><strong>Patents:</strong></p>
<ul>
<li>Principal inventor: 'Smarter Slug Flow Conditioning and Control'</li>
<li>Co-inventor: 'Technique for Production Enhancement with Downhole Monitoring of Artificially Lifted Wells'</li>
<li>Co-inventor: 'Wellbore real-time monitoring and analysis of fracture contribution'</li>
</ul>
<p><strong>Worldwide Experience in Training / Seminar / Workshop Deliveries:</strong></p>
<ul>
<li>Besides delivering several SPE webinars, ALRDC and SPE trainings globally, he has taught artificial lift at Texas Tech, Missouri S&amp;T, Louisiana State, U of Southern California, and U of Houston.</li>
<li>He has conducted seminars, bespoke trainings / workshops globally for practicing professionals:
<ul>
<li><u>Companies:</u> Basra Oil Company, ConocoPhillips, Chevron, EcoPetrol, Equinor, KOC, ONGC, LukOil, PDO, PDVSA, PEMEX, Petronas, Repsol, , Saudi Aramco, Shell, Sonatrech, QP, Tatneft, YPF, and others.</li>
<li><u>Countries:</u> USA, Algeria, Argentina, Bahrain, Brazil, Canada, China, Croatia, Congo, Ghana, India, Indonesia, Iraq, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Oman, Mexico, Norway, Qatar, Romania, Russia, Serbia, Saudi Arabia, S Korea, Tanzania, Thailand, Tunisia, Turkmenistan, UAE, Ukraine, Uzbekistan, Venezuela.</li>
<li>Virtual training provided for PetroEdge, ALRDC, School of Mines, Repsol, UEP-Pakistan, and others since pandemic.</li>
</ul>
</li>
</ul>
</div>
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POST TRAINING COACHING SUPPORT (OPTIONAL)

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<p>To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster.<br />
Request for further information post training support and fees applicable</p>
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Accreditions And Affliations

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EnergyEdge - Training for a Sustainable Energy Future

Renewable Energy

Oil & Gas

Gas Lift Design & Optimization using NODAL Analysis

Team of 10, pp

Normal Fee, pp

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

Data Engineering

Microsoft AZ-900

Microsoft Azure

Microsoft AZ-303

Microsoft AZ-700

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

Data Science

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

Machine Learning (ML)

Machine Learning for Apps Level 4

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

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

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

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

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

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

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

Python Machine Learning Crash Course for Beginners

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

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

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

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

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

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

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

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

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

Machine Learning for Absolute Beginners - Level 2

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

TensorFlow

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

The Complete Machine Learning Course with Python

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

Machine Learning - Use in Flutter

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

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

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

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

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

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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>

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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