Name: Computer Science: Coding, C++ , Python & HTML (30 in 1) CPD Accredited Courses!
Price: 310 GBP
Availability: InStock

<p><strong> 24-Hour Knowledge Knockdown! Prices Reduced Like Never Before </strong></p><p>Did you know that Python is the fastest-growing programming language in the world? In the UK alone, the demand for Python developers has grown by a staggering <strong>38% </strong>in the last year! If you're not one of them, now is the perfect time to get on board. And with our Diploma in Python Programming bundle, you can do just that.</p><p>This diploma takes you on a structured learning journey, starting with the fundamentals of Python programming. You'll master essential concepts like <strong>data structures, control flow, and functions</strong>. As you progress, you'll delve into advanced topics like data science, machine learning, and even explore its application in specialised fields like spatial analysis. By the program's end, you'll be proficient in writing Python scripts to solve complex problems, analyse data, and automate tasks. </p><p>With a single payment, you will gain access to <strong>Python Programming </strong>course, including <strong>10</strong> premium courses, <strong>a QLS Endorsed Hardcopy certificate </strong>(for the title course) and <strong>11 PDF certificates </strong>for Absolutely <strong>free</strong>.</p><p><strong>This Diploma in Python Programming </strong><strong>Bundle Package includes:</strong></p><ul><li><strong>Main Course:</strong> Diploma in Python Programming at QLS Level 5</li></ul><p><strong>10 Premium Additional CPD QS Accredited Courses -</strong></p><ul><li><strong>Course 01:</strong> Learn Programming with Python</li><li><strong>Course 02:</strong> Python Data Science</li><li><strong>Course 03: </strong>An Introduction to Graph Theory</li><li><strong>Course 04:</strong> Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li><strong>Course 05:</strong> Level-3 Machine Learning Course with Python</li><li><strong>Course 06: </strong>Learn to Use Python for Spatial Analysis in ArcGIS</li><li><strong>Course 07: </strong>Higher Order Functions in Python - Level 03</li><li><strong>Course 08:</strong> Easy to Advanced Data Structures</li><li><strong>Course 09:</strong> Introduction to Data Analysis</li><li><strong>Course 10: </strong>Business Intelligence and Data Mining Diploma</li></ul><p>With so much valuable content included, it's no wonder that this bundle is quickly becoming one of our most popular offerings. So why wait? Enrol today and start your journey towards becoming a Python expert.</p><p><strong>Learning Outcomes of Python Programming</strong></p><ul><li>Grasp fundamental Python concepts like variables, data types, operators, and control flow.</li><li>Master essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization.</li><li>Gain expertise in data analysis techniques and explore machine learning algorithms using Python.</li><li>Implement Python for cloud deployment using platforms like AWS or Azure (course dependent).</li><li>Utilise Python for spatial data analysis with ArcGIS (course dependent).</li><li>Develop proficiency in writing clean, efficient, and maintainable Python code.</li></ul><p><strong>Why Choose Us?</strong></p><ul><li>Get a <strong>Free QLS Endorsed Certificate</strong> upon completion of Python Programming</li><li>Get a <strong>free student ID card</strong> with Python Programming Training program (£10 postal charge will be applicable for international delivery)</li><li>The Python Programming is <strong>affordable and simple</strong> to understand</li><li>This course is <strong>entirely online, interactive lesson</strong> with voiceover audio</li><li>Get <strong>Lifetime access</strong> to the Python Programming course materials</li><li>The Python Programming comes with <strong>24/7 tutor support</strong></li></ul> <p>Start your learning journey straightaway!</p><p><strong>*** Course Curriculum ***</strong></p><p><strong>Main Course:</strong> Diploma in Python Programming at QLS Level 5</p><ul><li>Unit 01: Introduction To Python Programming</li><li>Unit 02: Getting Started With Python</li><li>Unit 03: Conditional Branching With Python</li><li>Unit 04: Importing External/Internal Library In Python</li><li>Unit 05: Project Rock Paper And Scissors</li><li>Unit 06: Strings Operation In Python</li><li>Unit 07: Date And Time In Python</li><li>Unit 08: File Handling, Read And Write Using Python</li><li>Unit 09: Data Storage Structures, Tuple, List And Dictionary</li><li>Unit 10: Writing User Functions In Python</li><li>Unit 11: Sending Mail</li><li>Unit 12: Import Tricks In Python</li><li>Unit 13: Import Operating System And Platform</li><li>Unit 14: Exceptions Handling In Python</li><li>Unit 15: Installing Packages And Scheduling In Python</li><li>Unit 16: Data Base In Python Using Sqlite</li><li>Unit 17: Running Program From Command Prompt And Jupyter Notebook</li><li>Unit 18: Conclusion</li></ul><p><strong>Course 01:</strong> Learn Programming with Python</p><p>Module 01</p><ul><li>A Installing Python FREE </li><li>Documentation </li><li>Command Line </li><li>Variables </li><li>Simple Python Syntax </li><li>Keywords </li><li>Import Module </li></ul><p>Module 02 </p><ul><li>Additional Topics </li><li>If Elif Else </li><li>Iterable </li><li>For </li><li>Loops </li><li>Execute </li><li>Exceptions </li></ul><p>Module 03 </p><ul><li>Data Types </li><li>Number Types </li><li>More Number Types </li><li>Strings </li><li>More Strings </li><li>Files </li><li>Lists </li><li>Dictionaries </li><li>Tuples </li><li>Sets </li></ul><p><strong>Course 02:</strong> Python Data Science</p><p>Unit 01: Introduction To Python Data Science </p><ul><li>Module 01: Introduction to Python Data Science </li><li>Module 02: Environment Setup </li></ul><p>Unit 02: Data Cleaning Packages </p><ul><li>Module 01: Numpy package for calculations </li><li>Module 02: Panda package for Data cleaning </li></ul><p>Unit 03: Data Visualization Packages </p><ul><li>Module 01: Matplotlib Data Visualization Part 1 </li><li>Module 02: Matplotlib Data Visualization Part 2 </li></ul><p><strong>=========&gt;&gt;&gt;&gt;&gt; And 8 More Courses &lt;&lt;&lt;&lt;&lt;=========</strong></p><p><strong>How will I get my Certificate?</strong></p><p>After successfully completing the course, you will be able to order your QLS Endorsed Certificates and CPD Accredited Certificates as proof of your achievement.</p><ul><li><strong>PDF Certificate: Free </strong>(Previously it was £12.99*11 = £143)</li><li><strong>QLS Endorsed Hard Copy Certificate: Free</strong> (For The Title Course: Previously it was £119)</li></ul> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>Anyone interested in learning more about the topic is advised to take this bundle. This bundle is ideal for:</p><ul><li>Individuals who want to become proficient Python programmers.</li><li>Professionals who want to enhance their programming skills.</li><li>Students who want to pursue a career in data science or machine learning.</li><li>Anyone interested in learning Python programming language.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There is no formal requirements to enrol in this course.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This bundle will help you to develop your knowledge and skills to pursue different careers, such as</p><ul><li>Junior Python Developer: £25,000 - £40,000 per year.</li><li>Data Analyst: £26,000 - £50,000 per year.</li><li>Machine Learning Engineer: £40,000 - £80,000 per year.</li><li>Data Scientist: £45,000 - £85,000 per year.</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Digital Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p>Upon passing the Course, you need to order a <strong>Digital Certificate</strong> for each of the courses inside this bundle as proof of your new skills that are accredited by <strong>CPD QS </strong>for Free.</p></div>
        <h4>CPD QS Accredited Certificate </h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p><em>Please note that International students have to pay an additional £10 as a shipment fee.</em></p></div>
        <h4>Diploma in Python Programming at QLS Level 5</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p><em>Please note that International students have to pay an additional £10 as a shipment fee.</em></p></div>

                        </div>

Academy for Health and Fitness

Diploma in Python Programming at QLS Level 5

General Admission

<p><strong> Sale Ends Today </strong></p><p><strong> Software Engineering: Software Programming, Web Design &amp; Development Admission Gifts </strong></p><p><strong> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter </strong></p><p>Software engineering has come a stage where it stands as the cornerstone of innovation and development of this era. You'll be amazed to know that the <strong>UK tech sector </strong>grew nearly <strong>6 times faster </strong>than the <strong>overall economy, </strong>all due to the advancement of this sector. So, the <strong>Software Engineering: Computer Programming, Web Design &amp; Development</strong> bundle is crafted to turn you into this versatile and sought-after software engineer, proficient in the latest technologies and coding languages from C# to Python, and beyond.</p><p>Start with foundational programming languages like <strong>C# </strong>and <strong>C++</strong>. Then you'll advance to complex frameworks and technologies such as <strong>Node.js</strong>, <strong>Three.js</strong>, and <strong>Python for Machine Learning</strong>. The courses include <strong>Web Development </strong>using <strong>HTML</strong>, <strong>CSS</strong>, <strong>PHP</strong>, and <strong>WordPress</strong>, to build and secure dynamic websites and applications. Each module is designed to build upon the last, ensuring a structured pathway from beginner to expert level.</p><p><strong>With this Software Engineering course, you will get 30 CPD Accredited PDF Certificates, Hard Copy Certificate of 1st Course and our exclusive student ID card absolutely free.</strong><strong><br /></strong></p><p><strong>Courses Are Included In This Software Engineering: Software Programming, Web Design &amp; Development Bundle: </strong></p><ul><li><strong> Course 01: </strong>C# Programming - Beginner to Advanced</li><li><strong> Course 02: </strong>C++ Development: The Complete Coding Guide</li><li><strong> Course 03: </strong>JavaScript Fundamentals</li><li><strong> Course 04: </strong>Refactor JavaScript</li><li><strong> Course 05: </strong>Node.JS: API Development with Swagger</li><li><strong> Course 06: </strong>Three.js &amp; WebGL 3D Programming</li><li><strong> Course 07: </strong>Python Programming Bible</li><li><strong> Course 08: </strong>Machine Learning with Python Course</li><li><strong> Course 09: </strong>Data Science and Visualisation with Machine Learning</li><li><strong> Course 10: </strong>Project on Deep Learning - Artificial Neural Network</li><li><strong> Course 11: </strong>Penetration Testing with OWASP ZAP</li><li><strong> Course 12: </strong>HTML Web Development Crash Course</li><li><strong> Course 13: </strong>CSS Web Development</li><li><strong> Course 14: </strong>Advanced Diploma in PHP Web Development with MySQL, GitHub &amp; Heroku</li><li><strong> Course 15: </strong>Advanced SAS Programming Using MacrosSQL</li><li><strong> Course 16: </strong>Diploma in User Experience UI/UX Design (Advanced)</li><li><strong> Course 17: </strong>Basics of WordPress: Create Unlimited Websites</li><li><strong> Course 18: </strong>Using WordPress for Affiliate Marketing</li><li><strong> Course 19: </strong>WordPress 5.0 for Beginners to Advanced</li><li><strong> Course 20: </strong>WordPress and SEO Online Course</li><li><strong> Course 21: </strong>How To Build Easy Drag &amp; Drop Landing Pages With WordPress</li><li><strong> Course 22: </strong>Make a WordPress WooCommerce Amazon Affiliate Store</li><li><strong> Course 23: </strong>WordPress Security Mastery</li><li><strong> Course 24: </strong>How To Build a WordPress Membership Site With Memberpress</li><li><strong> Course 25: </strong>Bootstrap - Responsive Web Design</li><li><strong> Course 26: </strong>Masterclass Bootstrap 5 Course - Responsive Web Design</li><li><strong> Course 27: </strong>ASP.Net MVC and Entity Framework</li><li><strong> Course 28: </strong>.NET Core API Development</li><li><strong> Course 29: </strong>How To Startup Your Own SaaS (Software As a Service) Company (SaaS Evolution)</li><li><strong> Course 30: </strong>Basic Jira and Confluence</li></ul><p>Don't just dream about creating the next big software innovation-make it a reality. Upgrade your skills with the <strong>Software Engineering: Computer Programming, Web Design &amp; Development</strong> course bundle and attempt to succeed in the fast-paced world of technology. Enrol today and start your journey towards becoming a pivotal player in the tech industry!</p><p><strong>Learning Outcomes of this Bundle:</strong></p><ul><li>Master programming fundamentals in C#, C++, JavaScript, and Python.</li><li>Develop comprehensive web applications using HTML, CSS, PHP, and WordPress.</li><li>Implement advanced web designs with Bootstrap for responsive layouts.</li><li>Utilise Python for machine learning and data visualisation projects.</li><li>Learn security practices and penetration testing with OWASP ZAP.</li><li>Design and deploy dynamic user interfaces with advanced UI/UX principles.</li></ul><p><strong>Why Prefer this Course?</strong></p><ul><li>Get a <strong>Free QLS Endorsed Certificate</strong> upon completion of the course</li><li>Get a <strong>Free Student ID Card</strong> with this training program (£10 postal charge will be applicable for international delivery)</li><li>The course is <strong>Affordable and Simple</strong> to understand</li><li>Get <strong>Lifetime Access</strong> to the course materials</li><li>The training program comes with <strong>24/7 Tutor Support</strong></li></ul> <p><strong>Start your learning journey straight away!</strong></p><p>The <strong>"Software Engineering: Computer Programming, Web Design &amp; Development"</strong> course bundle is especially designed to empower learners with the skills required to excel in the tech industry. By mastering <strong>programming fundamentals</strong> in diverse languages such as <strong>C#</strong>, <strong>C++</strong>, <strong>JavaScript</strong>, and <strong>Python</strong>, participants will gain a robust foundation in <strong>coding principles </strong>that are applicable across a variety of development environments. This foundational knowledge is crucial for <strong>troubleshooting </strong>and <strong>innovating </strong>within the rapidly evolving landscape of technology.</p><p>Beyond technical skills, the course also focuses on applying <strong>Python</strong> for <strong>Machine Learning</strong> and <strong>Data Visualisation </strong>projects, providing a competitive edge in fields like <strong>Data Science </strong>and <strong>Artificial Intelligence</strong>. Security practices and <strong>Penetration Testing </strong>with tools like <strong>OWASP ZAP</strong> further prepare learners to <strong>secure applications </strong>against <strong>potential threats</strong>. By completing this bundle, participants will not only enhance their coding and web development abilities but also acquire valuable skills in <strong>Security </strong>and <strong>UI/UX Design</strong>, making them well-rounded professionals ready for various challenges in the tech industry.</p><p>Moreover, this diploma offers learners the opportunity to acquire a <strong>Recognised Qualification </strong>that is highly valued in the field of <strong>Software Engineering</strong>. With this <strong>Certification</strong>, graduates are better positioned to pursue career advancement and higher responsibilities within the <strong>Software Engineering</strong> setting. The skills and knowledge gained from this course will enable learners to make meaningful contributions to <strong>Software Engineering</strong> related fields impacting their <strong>Software Engineering</strong> experiences and long-term development.</p><p><strong> Course Curriculum </strong></p><p><strong> Course 01: C# Programming - Beginner to Advanced</strong></p><ul><li>Module 01: Introduction</li><li>Module 02: Basics To Get Started</li><li>Module 03: Logic</li><li>Module 04: Methods (Basic)</li><li>Module 05: Classes (Basics)</li><li>Module 06: Variables (Basic)</li><li>Module 07: Error Handling</li><li>Module 08: Useful Methods</li><li>Module 09: Text (Advanced)</li><li>Module 10: Classes (Advanced)</li><li>Module 11: Binary (Advanced)</li><li>Module 12: Asynchronous (Advanced)</li><li>Module 13: Search In Objects (Linq)</li></ul><p><strong> Course 02: C++ Development: The Complete Coding Guide</strong></p><ul><li>Module 01: Introduction &amp; Setting Up</li><li>Module 02: Basics</li><li>Module 03: Operators</li><li>Module 04: String Streams</li><li>Module 05: Statements &amp; Loops</li><li>Module 06: Functions</li><li>Module 07: Advanced Data Types</li><li>Module 08: Classes</li><li>Module 09: Errors</li><li>Module 10: The Mighty Preprocessor</li><li>Module 11: File I/O</li><li>Module 12: Extras</li></ul><p><strong> Course 03: JavaScript Fundamentals</strong></p><ul><li>Module 01: Introduction</li><li>Module 02: Reasons of Employee Resignation</li><li>Module 03: What is JavaScript?</li><li>Module 04: Running JS Code</li><li>Module 05: Variables</li><li>Module 06: What is Talent Management</li><li>Module 07: Talent Management &amp;#8211; The Pillar of Successful Business</li><li>Module 08: Comparison Operators</li><li>Module 09: Responsibilities of Talent Management Manager &amp;#038; Professionals</li><li>Module 10: Function</li><li>Module 11: Object</li><li>Module 12: Methods</li><li>Module 13: While Loops</li><li>Module 14: Little Activity Before We Begin</li><li>Module 15: For Loops</li><li>Module 16: Arrays</li><li>Module 17: Welcome to the Course</li><li>Module 18: Multidimensional Arrays</li><li>Module 19: Introduction to the DOM API</li><li>Module 20: Conclusion</li></ul><p><strong>=========&gt;&gt;&gt;&gt;&gt; And 27 More Courses &lt;&lt;&lt;&lt;&lt;=========</strong></p><p><strong>How will I get my Certificate?</strong></p><p>After successfully completing the course, you will be able to order your Certificates as proof of your achievement.</p><ul><li><strong>PDF Certificate:</strong> Free (Previously it was £12.99*30 = £390)</li><li><strong>CPD Hard Copy Certificate: </strong>Free (For The First Course: Previously it was £29.99)</li></ul> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>Anyone interested in learning more about the topic is advised to take this bundle. This bundle is ideal for:</p><ul><li>Aspiring software developers.</li><li>Current IT professionals.</li><li>Web developers.</li><li>System engineers.</li><li>Tech enthusiasts.</li><li>Career changers.</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

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

                        <div id="career_path">
                            <p>After completing this bundle, you are to start your career or begin the next phase of your career.</p><ul><li>Software Developer: $50,000 - $120,000</li><li>Web Developer: $40,000 - $85,000</li><li>Front-End Developer: $50,000 - $110,000</li><li>Python Developer: $60,000 - $120,000</li><li>UI/UX Designer: $55,000 - $100,000</li><li>Systems Engineer: $60,000 - $130,000</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Online Course Marketing

Online Course Creation

Computing & Software Development Tools

Web Development

Software Engineering

Software Development

Software Engineering:  Software Programming, Web Design & Development

<strong>Master Data Science skills using Python and real time project and go from Beginner to Super Advance level</strong>
        <p>Want to become a good Data Scientist?  Then this is a right course for you.
This course was designed by IT professionals with a Master's in Mathematics and Data Science.  We cover complex theories, algorithms, and coding libraries in a very simple way so they can be easily grasped by any beginner.
We walk you step-by-step through the World of Data science. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science from beginner to advanced level.
All the codes and supporting files for this course are available at: https://github.com/PacktPublishing/Data-Statistics-with-Full-Stack-Python</p>
        <h2>What You Will Learn</h2><p>Master Data Science on Python<br>Learn to use Numpy and Pandas for Data Analysis<br>Learn all the math you need to understand Machine Learning algorithms<br>Real-world case studies<br>Learn to use Matplotlib for Python plotting<br>Learn to use Seaborn for statistical plots<br>Master end-to-end data science solutions<br>Learn all statistical concepts you need to become a Machine Learning ninja </p>
        <h2>Audience</h2><p>This course is for anyone who wants to become a Data Scientist.</p>
        <h2>Approach</h2><p>Exhaustive and packed with step-by-step instructions, working examples, and helpful advice, this course is divided into clear chunks so you can learn at your own pace and focus on your own area of interest.</p>
        <h2>Key Features</h2><p>Master complex theories, algorithms, and coding libraries in a very simple way * Master Data Science in Python</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/data-statistics-with-full-stack-python</p>
        <h2>About the Author</h2><h3>Geekshub Pvt. Ltd. </h3><p>Geekshub is an online education company in the field of big data and analytics. Their aim as a team is to provide the best skill-set to their customers to make them job-ready and prepare them to crack any challenge. They have the best trainers for cutting-edge technologies such as machine learning, deep learning, Natural Language Processing (NLP), reinforcement learning, and data science. Their instructors are people who graduated from IIT, MIT and Standford. They are passionate about teaching the topics using curated real-world case studies that calibrate the learning experience of students.</p>
        <h2>Course Outline</h2><h4>1. Python Fundamentals</h4><p></p><br><h4>2. Numpy</h4><p></p><br><h4>3. Pandas</h4><p></p><br><h4>4. Some Fun With Maths</h4><p></p><br><h4>5. Inferential Statistics</h4><p></p><br><h4>6. Hypothesis Testing</h4><p></p><br><h4>7. Data Visualization</h4><p></p><br><h4>8. Exploratory Data Analysis</h4><p></p><br><h4>9. Simple Linear Regression</h4><p></p><br><h4>10. Real World Problem - Investment Requirement Analysis for a Company</h4><p></p><br><h4>11. Loan Analysis Project</h4><p></p>

Packt

Statistics

Python

Master Data Science skills using Python and real time project and go from Beginner to Super Advance level

Data Statistics with Full Stack Python

<h2>ITIL® 4 Specialist: Create, Deliver and Support: On-Demand</h2>
 <p>The ITIL® 4 Specialist: Create, Deliver, and Support module is part of the Managing Professional stream for ITIL® 4. Candidates need to pass the related certification exam for working towards the Managing Professional (MP) designation. </p>
 <p>This course is based on the ITIL® 4 Specialist: Create, Deliver, and Support exam specifications from AXELOS. With the help of ITIL® 4 concepts and terminology, exercises, and examples included in the course, candidates acquire the relevant knowledge required to pass the certification exam.</p><h3>What You Will Learn</h3>
 <p>The learning objectives of the course are based on the following learning outcomes of the ITIL® 4 Specialist: Create, Deliver, and Support exam specification: </p>
 <ul>
 <li>Understand how to plan and build a service value stream to create, deliver, and support services </li>
 <li>Know how relevant ITIL® practices contribute to the creation, delivery, and support across the SVS and value streams </li>
 <li>Know how to create, deliver, and support services</li>
 </ul><section>
                    <h3>Organization and Culture </h3> <ul> <li>Organizational Structures  </li> <li>Team Culture  </li> <li>Continuous Improvement </li> <li>Collaborative Culture  </li> <li>Customer-Oriented Mindset  </li> <li>Positive Communication </li> </ul> <h3>Effective Teams </h3> <ul> <li>Capabilities, Roles, and Competencies  </li> <li>Workforce Planning  </li> <li>Employee Satisfaction Management  </li> <li>Results-Based Measuring and Reporting </li> </ul> <h3>Information Technology to Create, Deliver, and Support Service  </h3> <ul> <li>Integration and Data Sharing  </li> <li>Reporting and Advanced Analytics  </li> <li>Collaboration and Workflow  </li> <li>Robotic Process Automation  </li> <li>Artificial Intelligence and Machine Learning  </li> <li>CI / CD  </li> <li>Information Model </li> </ul> <h3>Value Stream </h3> <ul> <li>Anatomy of a Value Stream  </li> <li>Designing a Value Stream </li> <li>Value Stream Mapping </li> </ul> <h3>Value Stream to Create, Deliver, and Support Services </h3> <ul> <li>Value Stream for Creation of a New Service  </li> </ul> <h3>Value Stream for User Support </h3> <ul> <li>Value Stream Model for Restoration of a Live Service  </li> </ul> <h3>Prioritize and Manage Work </h3> <ul> <li>Managing Queues and Backlogs  </li> <li>Shift-Left Approach </li> <li>Prioritizing Work  </li> </ul> <h3>Commercial and Sourcing Considerations </h3> <ul> <li>Build or Buy  </li> <li>Sourcing Models  </li> <li>Service Integration and Management</li> </ul>


                </section>

IIL Europe Ltd

ITIL 4 Specialist: Create, Deliver and Support: On-Demand

<div>
</div>
<section>
<div>
<h2><strong>About this Virtual Instructor Led Training (VILT)</strong></h2>
<p>The energy industry has started its journey to be more data centric by embracing the industry 4.0 concept. As a result, data management - which was considered until recently as a back-office service to support geoscience, reservoir management, engineering, production and maintenance - is now given the spotlight!</p>
<p>To become an active stakeholder in this important transition in E&amp;P data management, it is necessary to understand the new technical opportunities offered by the Cloud, Artificial Intelligence and how data governance can pave the way towards more reliable and resilient processes within E&amp;P domain.</p>
<p>Several key questions that need to be addressed:</p>
<ol>
<li>Why place more focus on data assets?</li>
<li>Is data management just about serving geoscientists or engineers with fresh data?</li>
<li>What is the value of data management in the E&amp;P sector for decision making?</li>
<li>How to convince the data consumers that the data we provide is reliable?</li>
<li>Is the data architecture of my organization appropriate and sustainable?</li>
</ol>
<p>The purpose of this 5 half-day Virtual Instructor Led Training (VILT) course is to present the data challenges facing the energy organizations today and see how they practically solve them. The backbone of this course is based on the <strong>DAMA Book of Knowledge for Data Management</strong>. The main data management activities are described in sequence with a particular focus on recent technological developments.</p>
</div>
</section>
<section>
<div id="accordion">
<div>
<div>

Training Objectives 

</div>
<div id="collapseOne">
<div>
<p>Upon completion of this VILT course, the participants will be able to:</p>
<ul>
<li>Understand why the data asset is now considered as a main asset by energy organizations</li>
<li>Appreciate the importance of data governance and become an active stakeholder of it</li>
<li>Understand the architecture and implementation of data structure in their professional environment</li>
<li>Get familiarized with the more important data management activities such as data security and data quality</li>
<li>Integrate their subsurface and surface engineering skills with the data managements concepts</li>
</ul>
<p>This VILT course is unique on several points:</p>
<ul>
<li>All notions are explained by some short presentations. For each of them, a set of video, exercises, quizzes will be provided to help develop an engaging experience between the trainer and the participants</li>
<li>A pre-course questionnaire to help the trainer focus on the participants' needs and learning objectives</li>
<li>A detailed reference manual</li>
<li>A lexicon of terms for data-management</li>
<li>Limited class size to encourage the interactivity</li>
</ul>
</div>
</div>
</div>
<div>
<div>

Target Audience 

</div>
<div id="collapseTwo">
<div>
<p>This VILT course is intended for:</p>
<ul>
<li>Junior/new data managers</li>
<li>Geoscientists</li>
<li>Reservoir engineers</li>
<li>Producers</li>
<li>Maintenance specialists</li>
<li>Construction specialists</li>
<li>Human resources</li>
<li>Legal</li>
</ul>
</div>
</div>
</div>
<div>
<div>

Course Level 

</div>
<div id="collapsecl">
<div>
<ul>
<li>Basic or Foundation</li>
</ul>
</div>
</div>
</div>
<div>
<div>

Training Methods 

</div>
<div id="collapseThree">
<div>
<p>The VILT course will be delivered online in <strong>5 half-days consisting 4 hours per day</strong>, with 2 breaks of 10 minutes per day.</p>
<p>Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total).</p>
</div>
</div>
</div>
<div>
<div>

Trainer

</div>
<div id="collapseFive">
<div>
<p>Your expert course leader is a geologist by education who has dedicated his career to subsurface data management services. In 2016, he initiated a tech startup dedicated to Data Management using Artificial Intelligence (AI) tools. He is heavily involved in developing business plans, pricing strategies, partnerships, marketing and SEO, and is the co-author of several Machine Learning publications. He also delivers training on Data Management and Data Science to students and professionals.</p>
<p>Based in France, he was formerly Vice President, Sales &amp; Marketing at CGG where he was in charge of the Data Management Services strategy, Sales Manager at Spie O&amp;G Services where he initiated the Geoscience technical assistance activities and Product Manager of interactive seismic inversion software design and marketing at Paradigm.</p>
<p> </p>
<p> </p>
<p> </p>
</div>
</div>
</div>
<div>
<div>

POST TRAINING COACHING SUPPORT (OPTIONAL)

</div>
<div id="collapseCS">
<div>
<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 about post training coaching support and fees applicable for this.</p>
</div>
</div>
</div>
<div>
<div>

Accreditions And Affliations

</div>
<div id="collapseSeven">
<div>
               </div>
</div>
</div>
</div>
</section>

EnergyEdge - Training for a Sustainable Energy Future

Renewable Energy

Oil & Gas

Fundamentals of E&P Data Management

Team of 10, pp

Normal Fee, pp

<strong>Are you intrigued by how speech recognition is driving the growth of the AI market? This course is a reliable guide if you're looking to pursue a career as a speech recognition professional and understand industry best practices.</strong>
        <p>Are you intrigued by how speech recognition is driving the growth of the AI market? This course is a reliable guide if you're looking to pursue a career as a speech recognition professional and understand industry best practices. You'll learn the science of applying machine learning algorithms to process large amounts of speech data. As you progress, the course will get you up to speed with automated speech recognition. Later, you'll delve into speech translation, understanding how to work through speech-to-speech translation. Toward the concluding sections, you'll focus on voice conversion, exploring everything from Phonetic SID System to speaker identification. Throughout the course, you'll encounter practice questions to help you reinforce your knowledge.
By the end of this course, you'll be well-versed with speech recognition and be able to apply what you've learned in the real world.
The resources for this course are available at https://github.com/PacktPublishing/Speech-Recognition-A-Z-with-Hands-on</p>
        <h2>What You Will Learn</h2><p>Develop the skills you need to become a speech recognition professional<br>Get to grips with digitizing and recording speech signals<br>Discover how to build a natural language model<br>Understand the problem statement of converting speech to text<br>Work through speech-to-speech translation<br>Create a speech recognition project</p>
        <h2>Audience</h2><p>This course is for data analysts, data scientists, machine learning engineers, deep learning engineers, or anyone with basic knowledge of data analysis who has experience in creating models using machine learning.</p>
        <h2>Approach</h2><p>This is a hands-on course to help you develop the skills you need to become a speech recognition professional. It features plenty of practical examples and practice questions to ensure you learn easily.</p>
        <h2>Key Features</h2><p>Understand what speech processing is and explore the different types of speech recognition * Explore the complexities of speech recognition * Understand the difference between acoustic phonetics and articulatory phonetics</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/speech-recognition-a-z-with-hands-on</p>
        <h2>About the Author</h2><h3>Learnkart Technology Private Limited</h3><p>Learnkart understands the importance of upskilling and its impact on you. They pride themselves in creating specialized e-learning courses that will not only prepare you for certification exams but also help you gain hands-on knowledge for real-world applications. Thousands of students have developed their skills with Learnkart through a variety of courses, such as PgMP, Risk Management Professional (RMP), speech recognition, Python, Amazon Web Services (AWS), and more. Learnkart regularly adds courses to its portfolio to lend more value to your learning experience.</p>
        <h2>Course Outline</h2><h4>1. Speech and its Types</h4><p></p><br><h4>2. Automated Speech Recognition</h4><p></p><br><h4>3. Speech to Text</h4><p></p><br><h4>4. Text to Speech</h4><p></p><br><h4>5. Multilingual Speech Processing</h4><p></p><br><h4>6. Speech Translation</h4><p></p><br><h4>7. Spoken Dialog System</h4><p></p><br><h4>8. Voice Conversion</h4><p></p>

Are you intrigued by how speech recognition is driving the growth of the AI market? This course is a reliable guide if you're looking to pursue a career as a speech recognition professional and understand industry best practices.

Speech Recognition A-Z with Hands-On

<p><strong>24 Hours Left! Don't Let Year-End Deals Slip Away - Enrol Now!</strong></p><p>Embark on a journey to unravel the mysteries of Hydrology with the "ArcGIS for Hydrology" bundle! Whether you're an environmentalist, geologist, or simply curious about the world around you, this bundle has something for everyone. With 11 comprehensive courses, you can explore the depths of hydrology, spatial analysis, data visualisation, and more.</p><p>The first course in the bundle is QLS-endorsed, ensuring that you receive a hardcopy certificate upon completion - a testament to your commitment to learning and expanding your knowledge base. But that's just the beginning. The remaining 10 courses are CPD-QS accredited, which means you can be sure that you're gaining valuable insights and knowledge from the best in the industry.</p><p>Through the bundle, you'll learn about a range of tools, techniques, and methodologies that will enhance your understanding of hydrology and enable you to create impactful solutions. From spatial data visualisation and machine learning to web scraping and mapping dam levels, the bundle covers everything you need to know to be successful.</p><p>So, whether you're a seasoned professional or a curious beginner, join the ArcGIS for Hydrology bundle and immerse yourself in a world of knowledge and discovery. Let the courses take you on an enriching journey, expanding your mind and providing you with the skills and insights necessary to make a positive impact in the world of Hydrology. Enrol right now!</p><p><strong>This </strong><strong>ArcGIS for Hydrology</strong><strong> Bundle Package includes:</strong></p><ul><li><strong>Course 01: </strong>Diploma in ArcGIS for Hydrology at QLS Level 4</li></ul><p><strong>10 Premium Additional CPD QS Accredited Courses -</strong></p><ul><li><strong>Course 01: </strong>QGIS Cartography</li><li><strong>Course 02: </strong>Spatial Analysis in ArcGIS</li><li><strong>Course 03: </strong>Remote Sensing in ArcGIS</li><li><strong>Course 04: </strong>Python Data Science</li><li><strong>Course 05: </strong>Business Intelligence and Data Mining Diploma</li><li><strong>Course 06: </strong>Spatial Data Visualization and Machine Learning in Python Level 4</li><li><strong>Course 07: </strong>Create Smart Maps in Python and Leaflet Level 3</li><li><strong>Course 08: </strong>Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4</li><li><strong>Course 09: </strong>Maps in R Shiny and Leaflet</li><li><strong>Course 10: </strong>The Basics of ArcGIS for Hydrology</li></ul><p><strong>Why Prefer This </strong><strong>ArcGIS for Hydrology</strong><strong> Bundle?</strong></p><ul><li>You will receive a completely free certificate from the Quality Licence Scheme</li><li>Option to purchase 10 additional certificates accredited by CPD</li><li>Get a free Student ID Card - (£10 postal charges will be applicable for international delivery)</li><li>Free assessments and immediate success results</li><li>24/7 Tutor Support</li></ul> <p><strong>After taking this </strong><strong>ArcGIS for Hydrology</strong><strong> bundle courses, you will be able to learn:</strong></p><ul><li>Develop a deep understanding of hydrology and its applications</li><li>Master the use of ArcGIS and QGIS for cartography and spatial analysis</li><li>Learn how to use Python for data science, machine learning, and web scraping</li><li>Enhance your data visualisation skills in ArcGIS and Python</li><li>Gain expertise in creating smart maps and web applications using Leaflet and R Shiny</li><li>Understand the basics of hydrology in ArcGIS for beginners</li><li>Earn QLS-endorsed and CPD-QS accredited certificates to showcase your skills and knowledge.</li></ul><p><strong>***Curriculum breakdown of ArcGIS for Hydrology</strong><strong>***</strong></p><p><strong>Section 01: Introduction to ArcGIS software</strong></p><ul><li>Introduction to GIS</li><li>Introduction to ArcGIS software</li><li>Selection by Location, attributes, clip features and tables to Excel</li><li>Performing actions on the data: change the projections, dissolve, clip</li><li>From .TXT and .DXF to shapefile</li><li>Calculations with attribute table and KML files in ArcMap</li><li>Export from ArcMap to PDF</li><li>ArcScene 3D</li><li>GIS Example Test</li><li>AutoCAD fixing polygon coordinates</li></ul><p><strong>Section 02: ArcGIS for Hydrology</strong></p><ul><li>Download hydrological data</li><li>Calculate flow direction and flow accumulation</li><li>Watershed delineation</li><li>Clip layers to watershed boundary</li><li>Stream segments and catchments areas</li><li>Stream order convert to vector</li><li>Spatial join to catchments</li><li>Join stream id to stream order</li><li>Map data and initial layout</li><li>Add and format map elements</li></ul><p><strong>How is the </strong><strong>ArcGIS for Hydrology</strong><strong>Bundle Assessment Process?</strong></p><p>You have to complete the assignment questions given at the end of the 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.</p><p>You will be entitled to claim a certificate endorsed by the Quality Licence Scheme after you successfully pass the exams.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        220 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 bundle is perfect for:</p><ul><li>Environmentalists, geologists, hydrologists, and scientists</li><li>GIS professionals, software engineers, and developers</li><li>Students and researchers in the fields of hydrology and environmental sciences</li><li>Anyone interested in learning about hydrology and its practical applications.</li></ul>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Here are a few options that people explore in this industry:</p><ul><li>GIS Analyst: £22,000 - £36,000 per year</li><li>Hydrologist: £25,000 - £45,000 per year</li><li>Environmental Scientist: £24,000 - £40,000 per year</li><li>Data Scientist: £35,000 - £75,000 per year</li><li>Web Developer: £24,000 - £50,000 per year</li><li>Machine Learning Engineer: £45,000 - £85,000 per year</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD QS Accredited Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p>Upon successfully completing the Bundle, you will need to place an order to receive a PDF Certificate for each course within the bundle. These certificates serve as proof of your newly acquired skills, accredited by CPD QS. Also, the certificates are recognised throughout the UK and internationally.</p></div>
        <h4>CPD QS Accredited Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p>International students are subject to a £10 delivery fee for their orders, based on their location.</p></div>
        <h4>Diploma in ArcGIS for Hydrology at QLS Level 4</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

ArcGIS for Hydrology Diploma - CPD Certified

<strong>The course is crafted to reflect the most in-demand workplace skills. It will help you understand all the essential concepts and methodologies with regards to PySpark. This course provides a detailed compilation of all the basics, which will motivate you to make quick progress and experience much more than what you have learned.</strong>
        <p>The hottest buzzwords in the Big Data analytics industry are Python and Apache Spark. PySpark supports the collaboration of Python and Apache Spark. In this course, you'll start right from the basics and proceed to the advanced levels of data analysis. From cleaning data to building features and implementing machine learning (ML) models, you'll learn how to execute end-to-end workflows using PySpark.

Right through the course, you'll be using PySpark to perform data analysis. You'll explore Spark RDDs, Dataframes, and a bit of Spark SQL queries. Also, you'll explore the transformations and actions that can be performed on the data using Spark RDDs and Dataframes. You'll also explore the ecosystem of Spark and Hadoop and their underlying architecture. You'll use the Databricks environment to run the Spark scripts and explore it as well.

Finally, you'll have a taste of Spark with AWS cloud. You'll see how we can leverage AWS storages, databases, computations, and how Spark can communicate with different AWS services and get its required data.

By the end of this course, you'll be able to understand and implement the concepts of PySpark and AWS to solve real-world problems.

The code bundles are available here: https://github.com/PacktPublishing/PySpark-and-AWS-Master-Big-Data-with-PySpark-and-AWS</p>
        <h2>What You Will Learn</h2><p>Learn the importance of Big Data<br>Explore the Spark and Hadoop architecture and ecosystem<br>Learn about PySpark Dataframes and PySpark DataFrames actions<br>Use PySpark DataFrames transformations<br>Apply collaborative filtering to develop a recommendation system using ALS models</p>
        <h2>Audience</h2><p>This course requires python programming experience as a prerequisite.</p>
        <h2>Approach</h2><p>In this learning-by-doing course, every theoretical explanation is followed by practical implementation. At the end of each concept, homework/tasks/activities/quizzes along with solutions are assigned. This is to evaluate and promote your learning based on the previous concepts and methods you have learned. Most of these activities will be coding-based, as the aim is to get you up and running with implementations.</p>
        <h2>Key Features</h2><p>Relate the concepts and practical aspects of Spark and AWS with real-world problems * Implement any project that requires PySpark knowledge from scratch * Know the theory and practical aspects of PySpark and AWS</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/PySpark-and-AWS-Master-Big-Data-with-PySpark-and-AWS</p>
        <h2>About the Author</h2><h3>AI Sciences</h3><p>AI Sciences are a group of experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.

AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science.

They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.

Their courses have successfully helped more than 100,000 students master AI and data science.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Introduction to Hadoop, Spark EcoSystems and Architectures</h4><p></p><br><h4>3. Spark RDDs</h4><p></p><br><h4>4. Spark DFs</h4><p></p><br><h4>5. Collaborative filtering</h4><p></p><br><h4>6. Spark Streaming</h4><p></p><br><h4>7. ETL Pipeline</h4><p></p><br><h4>8. Project - Change Data Capture / Replication On Going</h4><p></p>

Java

Apache Spark

Big Data

The course is crafted to reflect the most in-demand workplace skills. It will help you understand all the essential concepts and methodologies with regards to PySpark. This course provides a detailed compilation of all the basics, which will motivate you to make quick progress and experience much more than what you have learned.

PySpark and AWS: Master Big Data with PySpark and AWS

<div id="course-pills-1">Duration<p>1 Days</p><p>6 CPD hours</p>This course is intended for<p></p><p>The audience for this course includes software developers and data scientists who need to use large language models for generative AI. Some programming experience is recommended, but the course will be valuable to anyone seeking to understand how the Azure OpenAI service can be used to implement generative AI solutions.</p><p></p><p></p><div> <p>Note</p> <p>Generative AI is a fast-evolving field of artificial intelligence, and the Azure OpenAI service is subject to frequent changes. The course materials are maintained to reflect the latest version of the service at the time of writing.</p> </div> <p>Azure OpenAI Service provides access to OpenAI's powerful large language models such as GPT; the model behind the popular ChatGPT service. These models enable various natural language processing (NLP) solutions to understand, converse, and generate content. Users can access the service through REST APIs, SDKs, and Azure OpenAI Studio. In this course, you'll learn how to provision Azure OpenAI service, deploy models, and use them in generative AI applications.</p><p></p>Prerequisites<p></p><ul><li> Familiarity with Azure and the Azure portal.</li> <li>Experience programming with C# or Python. </li></ul><p></p></div><div id="course-pills-2"><h3>1 - Get started with Azure OpenAI Service</h3> <ul> <li>Access Azure OpenAI Service</li> <li>Use Azure OpenAI Studio</li> <li>Explore types of generative AI models</li> <li>Deploy generative AI models</li> <li>Use prompts to get completions from models</li> <li>Test models in Azure OpenAI Studio's playgrounds</li> </ul> <h3>2 - Build natural language solutions with Azure OpenAI Service</h3> <ul> <li>Integrate Azure OpenAI into your app</li> <li>Use Azure OpenAI REST API</li> <li>Use Azure OpenAI SDK</li> </ul> <h3>3 - Apply prompt engineering with Azure OpenAI Service</h3> <ul> <li>Understand prompt engineering</li> <li>Write more effective prompts</li> <li>Provide context to improve accuracy</li> </ul> <h3>4 - Generate code with Azure OpenAI Service</h3> <ul> <li>Construct code from natural language</li> <li>Complete code and assist the development process</li> <li>Fix bugs and improve your code</li> </ul> <h3>5 - Generate images with Azure OpenAI Service</h3> <ul> <li>What is DALL-E?</li> <li>Explore DALL-E in Azure OpenAI Studio</li> <li>Use the Azure OpenAI REST API to consume DALL-E models</li> </ul> <h3>6 - Use your own data with Azure OpenAI Service</h3> <ul> <li>Understand how to use your own data</li> <li>Add your own data source</li> <li>Chat with your model using your own data</li> </ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans AI-050T00: Develop Generative AI Solutions with Azure OpenAI Service training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the AI-050T00: Develop Generative AI Solutions with Azure OpenAI Service 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

Generative AI

NLP Practitioner

Microsoft Azure

Azure Machine Learning

Azure Cognitive Services

AI-050T00 Develop Generative AI Solutions with Azure OpenAI Service

<p>Feeling Stuck in Your Career? The Computer Science: Coding, C++, Python &amp; HTML (30 in 1) CPD Accredited Courses! Bundle is Your Skill-Building Solution. This exceptional collection of <strong>30 premium courses</strong> is designed to encourage growth and improve your career opportunities. Suited to meet different interests and goals, the Computer Science: Coding, C++, Python &amp; HTML bundle provides an engaging learning experience, helping you learn skills across various disciplines.</p><p>With Computer Science: Coding, C++, Python &amp; HTML (30 in 1) CPD Accredited Courses! Bundle, you'll have a personalised journey that aligns with your career goals and interests. This comprehensive package helps you confidently tackle new challenges, whether entering a new field or enhancing your existing knowledge. The Computer Science: Coding, C++, Python &amp; HTML bundle is your gateway to expanding your career options, increasing job demand, and enhancing your skill set.</p><p>By enrolling in this bundle, you'll receive complimentary PDF certificates for all courses, adding value to your resume at no extra cost. Develop key skills and achieve important progress in your career and personal development. Start your journey today and experience the transformative impact of the Computer Science: Coding, C++, Python &amp; HTML (30 in 1) CPD Accredited Courses! bundle on your job life and career growth!</p><p><strong>This Computer Science: Coding, C++, Python &amp; HTML (30 in 1) CPD Accredited Courses! Bundle Comprises the Following CPD Accredited Courses:</strong></p><ul><li><strong>Course 01</strong>: Basic C# Coding</li><li><strong>Course 02</strong>: C++ Programming for Absolute Beginners</li><li><strong>Course 03</strong>: Computer Science with Python</li><li><strong>Course 04</strong>: C++ Development: The Complete Coding Guide</li><li><strong>Course 05</strong>: Computer Vision By Using C++ and OpenCV with GPU support</li><li><strong>Course 06</strong>: jQuery: JavaScript and AJAX Coding Bible</li><li><strong>Course 07</strong>: Ultimate PHP &amp; MySQL Web Development &amp; OOP Coding</li><li><strong>Course 08</strong>: Coding with Scratch</li><li><strong>Course 09</strong>: Learn CSS Coding from Scratch</li><li><strong>Course 10</strong>: Kotlin Masterclass Programming Course: Android Coding Bible</li><li><strong>Course 11</strong>: jQuery Masterclass Course: JavaScript and AJAX Coding Bible</li><li><strong>Course 12</strong>: Basic HTML 5</li><li><strong>Course 13</strong>: HTML Web Development Crash Course</li><li><strong>Course 14</strong>: Creative Web Design with HTML5, CSS3 and Bootstrap</li><li><strong>Course 15</strong>: Web Design for Beginners: Build Websites in HTML &amp; CSS</li><li><strong>Course 16</strong>: Python Intermediate Training</li><li><strong>Course 17</strong>: Learn Python, JavaScript, and Microsoft SQL for Data Science</li><li><strong>Course 18</strong>: Spatial Data Visualisation and Machine Learning in Python</li><li><strong>Course 19</strong>: Complete Python Machine Learning &amp; Data Science Fundamentals</li><li><strong>Course 20</strong>: Python Programming Bible | Networking, GUI, Email, XML, CGI</li><li><strong>Course 21</strong>: Deep Learning &amp; Neural Networks Python - Keras</li><li><strong>Course 22</strong>: The Complete Python 3 Course: Beginner to Advanced!</li><li><strong>Course 23</strong>: Machine Learning with Python Course</li><li><strong>Course 24</strong>: Python Advanced Training</li><li><strong>Course 25</strong>: Cloud Computing / CompTIA Cloud+ (CV0-002)</li><li><strong>Course 26</strong>: CompTIA IT Fundamentals ITF+ (FCO-U61)</li><li><strong>Course 27</strong>: Learn Ethical Hacking From A-Z: Beginner To Expert</li><li><strong>Course 28</strong>: Computer Simulation of Realistic Mathematical Models</li><li><strong>Course 29</strong>: Computer Hacking Forensic Investigator</li><li><strong>Course 30</strong>: Career Development Plan Fundamentals</li></ul><p><strong>What will make you stand out?</strong></p><p>Upon completion of this online Computer Science: Coding, C++, Python &amp; HTML (30 in 1) CPD Accredited Courses! Bundle, you will gain the following:</p><ul><li>CPD QS Accredited Proficiency with this Computer Science: Coding, C++, Python &amp; HTML Bundle</li><li>After successfully completing the Computer Science: Coding, C++, Python &amp; HTML bundle, you will receive a FREE PDF Certificate from REED as evidence of your newly acquired abilities.</li><li>Lifetime access to the whole collection of learning materials of this Computer Science: Coding, C++, Python &amp; HTML Bundle</li><li>The online test with immediate results</li><li>You can study and complete the Computer Science: Coding, C++, Python &amp; HTML bundle at your own pace.</li><li>Study for the Computer Science: Coding, C++, Python &amp; HTML bundle using any internet-connected device, such as a computer, tablet, or mobile device.</li></ul><p>The Computer Science: Coding, C++, Python &amp; HTML (30 in 1) CPD Accredited Courses! bundle is a premier learning resource, with each course module holding respected CPD accreditation, symbolising exceptional quality. The content is packed with knowledge and is regularly updated to ensure it remains relevant. This bundle offers not just education but a constantly improving learning experience designed to enrich both your personal and professional development.</p><p>Advance the future of learning with the Computer Science: Coding, C++, Python &amp; HTML bundle, a comprehensive, complete collection of 30 courses. Each course in the Computer Science: Coding, C++, Python &amp; HTML bundle has been handpicked by our experts to provide a broad range of learning opportunities. Together, these modules form an important and well-rounded learning experience.</p><p>Our mission is to deliver high-quality, accessible education for everyone. Whether you are starting your career, switching industries, or enhancing your professional skills, the Computer Science: Coding, C++, Python &amp; HTML bundle offers the flexibility and convenience to learn at your own pace. Make the Computer Science: Coding, C++, Python &amp; HTML package your trusted partner in your lifelong learning journey.</p><h2>
        
        <div>CPD</div>
    </h2>

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

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

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

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

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

                        </div>

Compliance Central

Computer Science: Coding, C++ , Python & HTML (30 in 1) CPD Accredited Courses!

<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 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

<div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
			<h2><b>Course Overview</b></h2>		</div>
				</div>
				<div>
				<div>
					<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>
				</div>
				</div>
				<div>
				<div>
					<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>
				</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div><div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
					</div>
				</div>
				<section>
						<div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
					<div>
											
															
											</div>
				</div>
				</div>
						</div>
					</div>
		</div>
				<div>
			<div>
							<div>
						<div>
				<div>
					<div>
											
															
											</div>
				</div>
				</div>
						</div>
					</div>
		</div>
				<div>
			<div>
							<div>
						<div>
				<div>
					<div>
											
															
											</div>
				</div>
				</div>
						</div>
					</div>
		</div>
				<div>
			<div>
							<div>
						<div>
				<div>
					<div>
											
															
											</div>
				</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>
		</section>
						</div>
					</div>
		</div>
								</div>
					</div>

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

<div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
					</div>
				</div>
				<div>
				<div>
					<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>
				</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div><div>
							<div>
					<div>
			<div>
							<div>
						<div>
				<div>
					<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>
				</div>
				</div>
						</div>
					</div>
		</div>
								</div>
					</div>

Learning & Development

Learning To Learn

Machine Learning - Use in Flutter

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

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

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

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

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

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

Deep Learning

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

632 Courses delivered Online

Diploma in Python Programming at QLS Level 5

By Academy for Health and Fitness

Software Engineering: Software Programming, Web Design & Development

By Academy for Health and Fitness

Data Statistics with Full Stack Python

By Packt

ITIL 4 Specialist: Create, Deliver and Support: On-Demand

By IIL Europe Ltd

Fundamentals of E&P Data Management

By EnergyEdge - Training for a Sustainable Energy Future

Speech Recognition A-Z with Hands-On

By Packt

ArcGIS for Hydrology Diploma - CPD Certified

By Academy for Health and Fitness

PySpark and AWS: Master Big Data with PySpark and AWS

By Packt

AI-050T00 Develop Generative AI Solutions with Azure OpenAI Service

By Nexus Human

Computer Science: Coding, C++ , Python & HTML (30 in 1) CPD Accredited Courses!

By Compliance Central