<strong>Learn the Microservices overall Architecture, Building Blocks, Key Advantages, Challenges and Industry Case Studies</strong>
        <p>Software is the engine, and data is the fuel. Until recently, many enterprise-level applications were designed, developed, and maintained as one giant monolithic application. With a once-a-year (or possibly twice-a-year) software release cycle, every small update is required to build, test, and deploy all application modules as one package.
Today more than ever, this traditional development approach is not good enough. It does not support the agility required by a fast-changing business landscape. Software updates should be released and deployed in weeks, days, and even hours.

Microservices are a new innovative development style and are already used by companies such as Netflix, Facebook, Amazon, and others. Microservices are building blocks for creating cloud-native applications, and therefore we will see more companies following this route. This course is designed to give you a solid theoretical understanding of the microservice concept.

All the code and supporting files for this course are available at - https://github.com/PacktPublishing/A-Beginner-s-Guide-to-a-Microservices-Architecture</p>
        <h2>What You Will Learn</h2><p>Challenges of traditional monolithic software development<br>Main building blocks of a single microservice<br>The concept of a microservice architecture<br>Cloud-native applications<br>Key advantages<br>Implementation challenges<br>Netflix: an industry case study</p>
        <h2>Audience</h2><p>This course is for software developers, IT professionals, cloud architects and product managers for cloud-native applications.</p>
        <h2>Approach</h2><p>Join us and learn why the industry is adopting the concept of microservices to create cloud-native applications. The course takes a very easy-to-understand approach for those who are new to microservices; all topics are explained very clearly without not necessary "going deeper" while it will come in the next steps. </p>
        <h2>Key Features</h2><p>Easy-to-follow overview of the microservice architecture * Understand the requirements for cloud-native applications * Real-life industry case studies </p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/a-beginner-s-guide-to-a-microservices-architecture</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</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Why Microservices?</p><p style='margin: 0'>Getting Started: Why Microservices?</p></td></tr><tr><td style='padding: 15px;'><p>2. Welcome!</p><p style='margin: 0'>Getting Started: Welcome!</p></td></tr><tr><td style='padding: 15px;'><p>3. Course Objectives and Structure</p><p style='margin: 0'>Getting Started: Course Objectives and Structure</p></td></tr></tbody></table><br><h4>2. Monolithic Applications</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Overview</p><p style='margin: 0'>Monolithic Applications: Overview</p></td></tr><tr><td style='padding: 15px;'><p>2. What is a Monolithic Application?</p><p style='margin: 0'>Monolithic Applications: What is a Monolithic Application?</p></td></tr><tr><td style='padding: 15px;'><p>3. #1 - Complex Release Cycles</p><p style='margin: 0'>Monolithic Applications: #1 - Complex Release Cycles</p></td></tr><tr><td style='padding: 15px;'><p>4. #2 - Scaling the Team</p><p style='margin: 0'>Monolithic Applications: #2 - Scaling the Team</p></td></tr><tr><td style='padding: 15px;'><p>5. #3 - Scaling the System</p><p style='margin: 0'>Monolithic Applications: #3 - Scaling the System</p></td></tr><tr><td style='padding: 15px;'><p>6. Summary</p><p style='margin: 0'>Monolithic Applications: Summary</p></td></tr></tbody></table><br><h4>3. The Microservices Revolution</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What are Micro-services?</p><p style='margin: 0'>The Microservices Revolution: What are Micro-services?</p></td></tr><tr><td style='padding: 15px;'><p>2. Looking inside a Micro-service</p><p style='margin: 0'>The Microservices Revolution: Looking inside a Micro-service</p></td></tr></tbody></table><br><h4>4. The Concept of a Microservices Architecture (MSA)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>The Concept of a Microservices Architecture (MSA): Introduction</p></td></tr><tr><td style='padding: 15px;'><p>2. Small Size and Loosely Coupled</p><p style='margin: 0'>The Concept of a Microservices Architecture (MSA): Small Size and Loosely Coupled</p></td></tr><tr><td style='padding: 15px;'><p>3. Organised around Business Capabilities</p><p style='margin: 0'>The Concept of a Microservices Architecture (MSA): Organised around Business Capabilities</p></td></tr><tr><td style='padding: 15px;'><p>4. Managed as Products</p><p style='margin: 0'>The Concept of a Microservices Architecture (MSA): Managed as Products</p></td></tr><tr><td style='padding: 15px;'><p>5. Smart Endpoints and Dumb Pipes</p><p style='margin: 0'>The Concept of a Microservices Architecture (MSA): Smart Endpoints and Dumb Pipes</p></td></tr></tbody></table><br><h4>5. What are the Key Benefits of Microservices?</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. #1 - Independently Developed and Deployed</p><p style='margin: 0'>What are the Key Benefits of Microservices?: Independently Developed and Deployed</p></td></tr><tr><td style='padding: 15px;'><p>2. #2 - Frequent and Faster Delivery</p><p style='margin: 0'>What are the Key Benefits of Microservices?: Frequent and Faster Delivery</p></td></tr><tr><td style='padding: 15px;'><p>3. #3 - The Best Tool for the Job</p><p style='margin: 0'>What are the Key Benefits of Microservices?: The Best Tool for the Job</p></td></tr><tr><td style='padding: 15px;'><p>4. #4 - More Granular Scaling</p><p style='margin: 0'>What are the Key Benefits of Microservices?: More Granular Scaling</p></td></tr><tr><td style='padding: 15px;'><p>5. And also, few Challenges</p><p style='margin: 0'>What are the Key Benefits of Microservices?: And also, few Challenges</p></td></tr></tbody></table><br><h4>6. Course Summary</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Netflix - Industry Case Study</p><p style='margin: 0'>Course Summary: Netflix - Industry Case Study</p></td></tr><tr><td style='padding: 15px;'><p>2. Quick Recap and Thank You!</p><p style='margin: 0'>Course Summary: Quick Recap and Thank You!</p></td></tr></tbody></table>

Packt

Cloud Computing

Microservices

Learn the Microservices overall Architecture, Building Blocks, Key Advantages, Challenges and Industry Case Studies

A Beginner's Guide to a Microservices Architecture

General Admission

<p>Are you interested in unlocking the secrets of heuristic algorithms in deep learning? Do you want to gain hands-on experience with R programming to build powerful and efficient models? If so, then our Essential Training on Deep Learning Heuristics using R is the perfect course for you!</p><p>In this course, you will learn how to apply heuristic algorithms to real-world problems in agriculture, cryptocurrencies, energy, and finance. With our step-by-step approach, you will gain a solid understanding of experimental design and be able to build models that can analyse large data sets and make predictions. Our experienced instructors will guide you through each module, ensuring that you have a thorough understanding of the concepts and can apply them to your own projects.</p><p>Whether you are a data analyst, machine learning engineer, or just someone interested in deep learning, this course is perfect for anyone looking to enhance their skills in R programming and heuristic algorithms. Join us today and discover the exciting world of deep learning heuristic algorithms with R!</p><p><strong>Learning Outcomes:</strong></p><ul><li>Develop an understanding of experimental design and how it applies to heuristics.</li><li>Learn how to apply heuristics to real-world problems in the agriculture, cryptocurrency, energy, and financial markets sectors.</li><li>Understand how to use R programming language for deep learning heuristics.</li><li>Develop skills in analyzing data sets and creating predictive models.</li><li>Learn how to implement different heuristics algorithms in R programming.</li></ul><p>The Essential Training on Deep Learning Heuristic using R is designed to provide students with a comprehensive understanding of heuristics and how they can be applied to various real-world problems. The course covers experimental design, data analysis, and predictive modelling using R programming language. Students will learn about different heuristics algorithms and how to implement them in R to solve complex problems in the agriculture, cryptocurrency, energy, and financial markets sectors.</p><p>Throughout the course, students will work on various projects that will help them develop practical skills in analyzing data sets and creating predictive models. They will learn how to design experiments, collect and analyse data, and create models that can be used to make predictions. By the end of the course, students will have a strong understanding of heuristics, and deep learning, and how to apply them to solve real-world problems.</p><p><strong>â±â±Essential Training on Deep Learning Heuristic using R</strong><strong> Course Curriculum </strong></p><ul><li><strong>Section 01: </strong>Experimental Design - Heuristics Project 1-Agriculture</li><li><strong>Section 02:</strong> Experimental Design - Heuristics Project 2-Cryptocurrencies</li><li><strong>Section 03: </strong>Experimental Design: Heuristics Project 3-Energy Sector</li><li><strong>Section 04: </strong>Experimental Design: Heuristics Project 4-Financial Markets</li></ul><p><strong>How is the course assessed?</strong></p><p>Upon completing an online module, you will immediately be given access to a specifically crafted MCQ test. For each test, the pass mark will be set to 60%.</p><p><strong>Exam &amp; Retakes:</strong><br />It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.</p><p><strong>Certification</strong><br />Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of __ GBP. £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <ul><li>Data scientists who want to learn how to apply deep learning heuristics to solve real-world problems.</li><li>Programmers who want to learn R programming language for data analysis and predictive modeling.</li><li>Professionals in the agriculture, cryptocurrency, energy, and financial markets sectors who want to apply heuristics to solve complex problems.</li><li>Students who want to learn about deep learning heuristics and how they can be applied to real-world problems.</li><li>Anyone interested in developing practical skills in data analysis and predictive modelling using R programming language.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal entry requirements for the course, with enrollment open to anyone!</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Deep Learning Engineer: £30,000 - £90,000</li><li>AI Researcher: £40,000 - £120,000</li><li>Data Scientist: £35,000 - £90,000</li><li>Machine Learning Engineer: £40,000 - £100,000</li><li>AI Architect: £50,000 - £150,000</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            £9
        </p>
            <div> <p>You can apply for a CPD Accredited PDF Certificate at the cost of £9.</p> </div>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            £15
        </p>
            <div> <p>Hard copy can be sent to you via post at the expense of £15.</p> </div>

                        </div>

One Education

Essential Training on Deep Learning Heuristic using R Course

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<p><span>Log Analytics plays a central role in Azure management by collecting telemetry and other data from a variety of sources and providing a query language and analytics engine that gives you insights into the operation of your applications and resources. </span></p>
<p><span>This <strong>Azure - Automation and Log Anayltics</strong> is designed to instruct you on Azure as it pertains to automation and log analytics. The course especially focuses on two major Azure services: Automation and Log Analytics. Throughout the course, you will learn how to use these two services for making your office work easy.  You will learn about creating automation accounts and creating runbooks.</span></p>
<p><span>In addition, the course will cover creating OMS workspaces and using the Azure Security Center. You will also monitor and systems to maintain availability and performance. </span></p>
<p><span>The course will provide an introduction to Azure Security Center, and also take a look at detection as a service. Finally, you will be familiarized with the Azure security Centre.  </span></p>
<h3><strong>What Will I Learn?</strong></h3>
<ul>
<li><span>Create Automation Accounts</span></li>
<li>Create Runbooks</li>
<li>Create and View OMS Workspaces</li>
<li><span>Understand the Azure Security Center</span></li>
</ul>
<h3><strong>Requirements</strong></h3>
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<li>Some prior knowledge in Azure is required, or the completion of Introduction to Azure course</li>
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					<td>Introduction</td>
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					<td>Azure Automation</td>
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					<td><span><i></i> 00:03:00</span></td>
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					<td><i></i></td>
					<td>Creating Azure Automation Accounts</td>
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					<td><span><i></i> 00:03:00</span></td>
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					<td><span><i></i> 00:04:00</span></td>
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					<td>Creating Runbooks</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
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					<td colspan="4">Log Analytics</td>
				</tr>
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					<td><i></i></td>
					<td>Log Analytics Overview</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
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					<td><i></i></td>
					<td>Creating and Viewing OMS Workspaces</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
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					<td colspan="4">Azure Security Center</td>
				</tr>
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					<td><i></i></td>
					<td>Intro to Azure Security Center</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
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					<td><i></i></td>
					<td>Detection as a Service</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
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					<td>ASC Investigations</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
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					<td colspan="4">Conclusion</td>
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					<td>Course Recap</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
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					<td colspan="4">Course Certification </td>
				</tr>
				</tbody></table>

iStudy UK

Microsoft AZ-900

Microsoft AZ-104

Microsoft Azure

Microsoft AZ-303

Azure Machine Learning

Azure Cognitive Services

Azure - Automation and Log Anayltics

<strong>This course will help you compose complex data visualization codes with easy-to-integrate Charts view for SwiftUI, which is supported by all Apple platforms that include iOS, iPadOS, macOS, and watchOS. Design charts using AreaMark, LineMark, and create multi-series charts using SwiftUI. Knowledge of SwiftUI and iOS programming is needed.</strong>
        <p>Apple unveiled a brand-new data visualization framework called Charts Framework at the Worldwide Developers Conference 2022. Charts enable developers to easily compose complex data visualizations with a few lines of code. The Charts view for SwiftUI is simple to integrate as any other view, so your data drives the changes for data visualization. Charts Framework is compatible with all Apple platforms, including iOS, iPadOS, macOS, and watchOS.

This course explores the new Charts Framework using SwiftUI 4 and iOS 16. We will create practical examples and explore options to configure and use charts framework to match the app theme. You will learn about Charts Framework and SwiftUI 4 features and understand various creatable simple charts in SwiftUI and different markers that spruce up charts and more advanced concepts like the multi-series charts in SwiftUI, chart customization, and finally range area charts and empty state charts.

Upon completion of this course, you will be adept in creating basic and multi-series charts. You will customize charts suited to your data visualization requirements and develop complex stacked, Candlestick, and combined charts and apply markers to enhance your chart data presentation.

All resources are available at: https://github.com/PacktPublishing/Mastering-Charts-Framework-in-SwiftUI-4-iOS-16</p>
        <h2>What You Will Learn</h2><p>Learn about Apple's Charts Framework in detail<br>Understand the new features introduced in SwiftUI 4<br>Know about the new features introduced in iOS 16<br>Learn to apply data visualization techniques in iOS 16 using SwiftUI 4<br>Learn to build applications with data visualization in iOS 16<br>Learn about the different kinds of charts available in the Charts Framework</p>
        <h2>Audience</h2><p>This intermediate- to advanced-level course caters to developers seeking to learn about Apple's newly introduced Charts Framework at WWDC 22. This course is intended for developers who want to incorporate data visualization into their apps, starting with iOS 16 and higher. It is also an ideal course for developers who want to include data visualization in their apps using SwiftUI. To garner the best benefits of this course, previous knowledge of SwiftUI and iOS programming is required.</p>
        <h2>Approach</h2><p>The course is designed to deliver high-definition video courses in a steady stream on focused topics related to iOS 16 and SwiftUI 4. The videos are demonstrative and instructive with frequent recapitulations of the concepts. The course explains each concept in a well-structured manner spread across 13 carefully delivered lessons explaining the subject matter comprehensively and lucidly.</p>
        <h2>Key Features</h2><p>Become adept in Apple's Charts Framework and understand new features of SwiftUI 4 and iOS 16 * Master data visualization skillets in iOS 16, SwiftUI, and create charts to present data visually * Learn from an expert with 40+ years of combined experience in developing apps and software</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Mastering-Charts-Framework-in-SwiftUI-4-iOS-16</p>
        <h2>About the Author</h2><h3>DevTechie LLC</h3><p>DevTechie LLC offers courses where we learn by doing and find practical approaches to solve common problems. DevTechie LLC has 40+ years of combined experience in developing apps and software for big tech companies to small start-ups. They believe in sharing the knowledge gained over the years and invite you to join them on this journey. DevTechie LLC covers end-to-end development for mobile, web, backend, data science, and machine learning.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Getting Started</h4><p></p><br><h4>3. Stacked Bar Chart</h4><p></p><br><h4>4. Multi-Series Bar Chart</h4><p></p><br><h4>5. Line Chart with LineMark</h4><p></p><br><h4>6. Multi-Series Line Chart with LineMark</h4><p></p><br><h4>7. Area Charts with AreaMark</h4><p></p><br><h4>8. Multi-Series Area Chart in SwiftUI 4</h4><p></p><br><h4>9. CandleStick Chart</h4><p></p><br><h4>10. Combined Chart</h4><p></p><br><h4>11. Range Area Chart</h4><p></p><br><h4>12. Chart Customizations</h4><p></p><br><h4>13. Empty State Chart</h4><p></p>

Swift

Mobile

iOS Development

This course will help you compose complex data visualization codes with easy-to-integrate Charts view for SwiftUI, which is supported by all Apple platforms that include iOS, iPadOS, macOS, and watchOS. Design charts using AreaMark, LineMark, and create multi-series charts using SwiftUI. Knowledge of SwiftUI and iOS programming is needed.

Mastering the Charts Framework in SwiftUI 4 & iOS 16

<strong>In this course, you will learn how to unlock the power of Generative AI and learn to generate passive income for profitable online ventures with ChatGPT. Gain the skills to automate tasks efficiently, generate revenue from your content, and uphold ethical AI practices. This course is perfect for beginners or anyone seeking to enhance their online ventures with AI technology.</strong>
        <p>Welcome! This comprehensive course is designed to help you discover the potential of ChatGPT and AI, and leverage it to create passive income streams.

This course offers a range of valuable skills across multiple sections. In the Writing section, you will discover how to utilize ChatGPT to create various content types, such as eBooks, articles, poetry, and sales copy for websites. Additionally, you will learn how to publish your eBooks on Kindle and earn revenue through Amazon Associates Program.

In the Photos and Digital Art section, you will explore how to leverage Midjourney to generate stock photos and phone backgrounds for Zedge. You will also learn how to dropship merchandise, which can create an additional source of passive income. In the Coding section, you will learn how to use ChatGPT to develop websites and design custom projects for clients on platforms like Upwork and Fiverr.

In the Social Media section, you will gain insight into how to automate ChatGPT using Zapier, which can assist in creating social media connections. In the Services section, you will acquire the skills to produce tailored nutrition and life coaching plans using ChatGPT. You will also learn how to read and evaluate resumes for others. In the Ethics section, you will delve into the significance of ethical AI and responsible use of AI for generating passive income.

By the end of this course, you will have the knowledge and skills to leverage the power of ChatGPT and AI to generate passive income in a variety of ways.</p>
        <h2>What You Will Learn</h2><p>Use ChatGPT to create children's story eBooks, write articles, and more<br>Use ChatGPT to create effective copywriting for ads and emails<br>Create beautiful digital art and photos with Midjourney and Zedge<br>Use ChatGPT to code a website and code for others using Upwork and Fiverr<br>Automate ChatGPT with Zapier and connect it to social media platforms<br>Create customized nutrition plans and life coaching plans</p>
        <h2>Audience</h2><p>This course is designed for individuals who are interested in using AI technology to generate passive income online. It is suitable for anyone who wants to learn how to utilize generative AI to create content for writing, photography, digital art, coding, and social media. Whether you are a freelancer, content creator, entrepreneur, or simply looking to diversify your income streams, this course will provide you with the tools and knowledge to leverage AI technology for profitable online ventures. No prior technical knowledge or coding experience is required, as the course will walk you through each step in a user-friendly and accessible manner.</p>
        <h2>Approach</h2><p>The course follows a practical, hands-on approach with step-by-step guidance, enabling learners to quickly acquire the skills needed to harness the power of Generative AI for their online ventures.</p>
        <h2>Key Features</h2><p>Utilization of ChatGPT AI for various tasks such as writing, coding, and nutrition planning * Emphasis on practical skills, such as creating eBooks and stock photos, and automating social media * Explore various creative mediums such as writing, digital art, and social media</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/How-to-Use-ChatGPT-and-Generative-AI-for-Passive-Income </p>
        <h2>About the Author</h2><h3>Delta X Technology LLC</h3><p>Delta X Technology LLC is owned by Justin B, a highly skilled software engineer and AI creator with 5 years of industry experience. With a strong background in both theoretical and practical aspects of software engineering, Justin has worked on several projects involving machine learning. As the owner of Delta X Technology LLC, he is dedicated to sharing his expertise with students and helping them develop the skills needed to succeed in the field of AI. With a passion for teaching and a wealth of real-world experience, Justin offers students a unique perspective and valuable insights into the latest trends and best practices in AI development.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Writing</h4><p></p><br><h4>3. Photos and Digital Art</h4><p></p><br><h4>4. Coding</h4><p></p><br><h4>5. Social Media</h4><p></p><br><h4>6. Services</h4><p></p><br><h4>7. Ethics</h4><p></p>

Coding

Generative AI

Ethics

Web Development

Writing

Freelancing

Automation

ChatGPT

In this course, you will learn how to unlock the power of Generative AI and learn to generate passive income for profitable online ventures with ChatGPT. Gain the skills to automate tasks efficiently, generate revenue from your content, and uphold ethical AI practices. This course is perfect for beginners or anyone seeking to enhance their online ventures with AI technology.

How to Use ChatGPT and Generative AI for Passive Income

<strong>This course is intended for beginner-level individuals who are fascinated about quantum computing and want to learn more about it. It uses Jupyter notebook and IBM Qiskit tool to execute your learning into the actual computation.</strong>
        <p>Quantum computers could help the development of new breakthroughs in science, medications, machine learning, material science, and finance, which will help mankind become the best civilization in the whole universe. In fact, quantum computing is so powerful that no one knows how to use its true potential and till now, no quantum algorithm is perfect. The hardware and code are still in development stages, providing great opportunities in the future for quantum computing professionals.

This course starts by introducing you to the basics of current classical computing technology that is based on bits/binary digits (0 and 1) and quantum computing (qubits), and how it is way much more advanced than earlier. Then you will install and get tested on working with the Jupyter notebook and IBM Qiskit in order to execute codes. Lastly, you will learn all the quantum computing concepts and their execution simultaneously in a much leaner, simpler, and more concise format.

By the end of this course, you will learn the basics of quantum computing through implementing it via IBM Qiskit, and you could be able to contribute yourself to the domain that is still in the development phase and be a part of great opportunities in the future for quantum computing professionals.

The resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Dummies-Guide-to-Practical-Quantum-Computing-with-IBM-Qiskit</p>
        <h2>What You Will Learn</h2><p>Learn essential details about quantum mechanics<br>Install and test Qiskit, the quantum framework by IBM<br>Code quantum circuit using Pauli x-gate and other gates<br>Learn eigenvalue and eigenvectors<br>Create a multi-qubit circuit using single-qubit gates<br>Learn the Deutsch-Jozsa (DJ) algorithm, which demonstrates quantum parallelism</p>
        <h2>Audience</h2><p>The course is best suited for beginners who want to start with practical quantum computing concepts.<br><br>Basic computer knowledge and enthusiasm about quantum computing are the only prerequisites to excel and extract the most out of this course.</p>
        <h2>Approach</h2><p>This course is divided into smaller modules followed by hands-on examples performed on IBM Qiskit documentation. Almost all of the illustrations and examples used in this course are from the IBM Qiskit documentation website, which may be further referred to at any point of time in the future.</p>
        <h2>Key Features</h2><p>Learn and understand about general quantum computing based on IBM Qiskit documentation * Learn quantum key distribution, which utilizes the unique properties of quantum systems * Learn quantum teleportation used for transferring quantum information from a sender to a receiver</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Dummies-Guide-to-Practical-Quantum-Computing-with-IBM-Qiskit</p>
        <h2>About the Author</h2><h3>Abhilash Nelson</h3><p>Abhilash Nelson is a pioneering, talented, and security-oriented Android/iOS mobile and PHP/Python web application developer with more than 8 years of IT experience involving designing, implementing, integrating, testing, and supporting impactful web and mobile applications. He has a master's degree in computer science and engineering and has PHP/Python programming experience, which is an added advantage for server-based Android and iOS client applications. Abhilash is currently a senior solution architect managing projects from start to finish to ensure high quality and innovative and functional design.</p>
        <h2>Course Outline</h2><h4>1. Course Introduction and Table of Contents</h4><p></p><br><h4>2. Introduction to Quantum Mechanics</h4><p></p><br><h4>3. Classical Bit vs Quantum Qubit</h4><p></p><br><h4>4. Creating, Retaining and Reading out Qubits</h4><p></p><br><h4>5. Vector and Matrix Quantum States</h4><p></p><br><h4>6. Classic Logic Gates Overview</h4><p></p><br><h4>7. Popular Quantum Frameworks</h4><p></p><br><h4>8. Installing Anaconda Python Distribution</h4><p></p><br><h4>9. Installing and Testing Qiskit</h4><p></p><br><h4>10. Pauli X-gate in Qiskit</h4><p></p><br><h4>11. Pauli X-gate input and output customizations</h4><p></p><br><h4>12. Pauli X-gate in Real IBM Quantum Computer</h4><p></p><br><h4>13. Pauli Matrixes as State Vectors</h4><p></p><br><h4>14. Pauli Y-gate Operations</h4><p></p><br><h4>15. Pauli Z-gate</h4><p></p><br><h4>16. Eigen Vectors of XYZ gates</h4><p></p><br><h4>17. Hadamard Gate Introduction</h4><p></p><br><h4>18. Hadamard Gate in Qiskit</h4><p></p><br><h4>19. Hadamard Gate Exercises</h4><p></p><br><h4>20. H gate in Real Quantum Computer</h4><p></p><br><h4>21. R phi Gate</h4><p></p><br><h4>22. S and T Gates</h4><p></p><br><h4>23. U and I Gates</h4><p></p><br><h4>24. Multi Qubit states introduction</h4><p></p><br><h4>25. Representing Multi Qubit States</h4><p></p><br><h4>26. Multi Qubit Circuit using Single Qubit Gates - sample circuit 1</h4><p></p><br><h4>27. Multi Qubit Circuit using Single Qubit Gates - sample circuit 2</h4><p></p><br><h4>28. CNOT gate with classical Qubits</h4><p></p><br><h4>29. CNOT gate with control qubit superposition</h4><p></p><br><h4>30. CNOT gate with control qubit superposition - In Real Quantum Computer</h4><p></p><br><h4>31. CNOT gate with both qubit superposition</h4><p></p><br><h4>32. CNOT gate with both qubit superposition target x</h4><p></p><br><h4>33. CNOT Circuit Identities</h4><p></p><br><h4>34. CZ Circuit Identity</h4><p></p><br><h4>35. CY Circuit Identity</h4><p></p><br><h4>36. SWAP Circuit Identity</h4><p></p><br><h4>37. Toffoli Gate</h4><p></p><br><h4>38. Toffoli Circuit Identity</h4><p></p><br><h4>39. DJ Problem Overview</h4><p></p><br><h4>40. DJ Algorithm Design</h4><p></p><br><h4>41. DJ Algorithm Implementation</h4><p></p><br><h4>42. Quantum Cryptography : Quantum Key Distribution</h4><p></p><br><h4>43. Quantum Teleportation Theory</h4><p></p><br><h4>44. Further Learning and Resources</h4><p></p>

Qiskit

Quantum Computing

Programming

This course is intended for beginner-level individuals who are fascinated about quantum computing and want to learn more about it. It uses Jupyter notebook and IBM Qiskit tool to execute your learning into the actual computation.

Beginners' Guide to Practical Quantum Computing with IBM Qiskit

<p><strong>Python programmers</strong> are in great demand for a variety of reasons, including the language's widespread use and popularity as well as the fact that Python has emerged as a viable alternative in several fields. Developing web applications, working with data science, and machine learning can be done through Python codes. As a result, expertise in Python can open up a wide range of opportunities for you. If you want to develop your skills from scratch, or just want to refresh your old skills, we got you covered. We offer a <strong>Python Programming at QLS Level 7</strong> course discussing the basics to advanced techniques.</p><p><strong> Enrol this course and achieve the Quality Licence Scheme endorsed certificate </strong></p><p>Learners who are highly motivated and computer literate and who want to learn how to program in python should enrol in this course. It is great for developing your coding skills. We focused on a vast array of tools and technologies in this extensive course, including:</p><ul><li>Introduction to Python Programming from A-Z</li><li>Getting Familiar with Python</li><li>Basic Data Types</li><li>Python Operators</li><li>Advanced-Data Types</li><li>Control Flow Part 1</li><li>Control Flow Part 2</li><li>Functions</li><li>User Input and Error Handling</li><li>Python Advanced Functions</li><li>Python Scripting and Libraries</li><li>NumPy</li><li>Pandas</li><li>Introduction to OOP</li><li>Advanced OOP</li></ul><p>In the UK, there are more than <strong>750 new job posts for software engineers</strong> every day, and Python is the second-most in-demand programming language among companies. So, if you want to manage your future and make money by learning new skills, Python can be your best bet.</p><p>This course training includes 1 of the best-selling course endorsed by the Quality Licence Scheme with an endorsed certificate of achievement, expert assistance, and course assessment. Enjoy 12 months of free access to this course and master a range of in-demand professional skills to advance your career. <strong>No hidden costs - simply enrol this course.</strong></p><p><strong>Our course is endorsed by the Quality Licence Scheme, ensuring your newly acquired skills will enhance your professional development. This course is accredited by CPD.</strong></p><p>Dedicated learning assistance and 24/7 customer support are available to all students with this premium quality this course. Get the help you require and the answers to all your queries throughout the comprehensive syllabus of this course.</p><p>With this Python Programming at QLS Level 7 training, success becomes much easier and enables you to monetise your skills. This course is designed for both part-time and full-time students and can be completed at a pace that suits your learning style.</p><p><strong>Curriculum Breakdown:</strong></p><ul><li><strong>Module 01: Introduction to Python Programming from A-Z<br /></strong></li><li><strong>Module 02: Getting Familiar with Python<br /></strong></li><li><strong>Module 03: Basic Data Types<br /></strong></li><li><strong>Module 04: Python Operators<br /></strong></li><li><strong>Module 05: Advanced Data Types<br /></strong></li><li><strong>Module 06: Control Flow Part 1<br /></strong></li><li><strong>Module 07: Control Flow Part 2<br /></strong></li><li><strong>Module 08: Python Functions<br /></strong></li><li><strong>Module 09: User Input and Error Handling<br /></strong></li><li><strong>Module 10: Python Advanced Functions<br /></strong></li><li><strong>Module 11: Python Scripting and Libraries<br /></strong></li><li><strong>Module 12: NumPy<br /></strong></li><li><strong>Module 13: Pandas<br /></strong></li><li><strong>Module 14: Introduction to OOP<br /></strong></li><li><strong>Module 15: Advanced OOP<br /></strong></li><li><strong>Module 16: Starting a Career in Python</strong></li></ul><p><strong>Key Highlights</strong></p><ul><li>The Quality Licence Scheme ensures that the course you enrolled in, Python Programming at QLS Level 7, is of the highest quality.</li><li>It helps you to get recognised by employers easily.</li><li>The endorsed certificate of this course will make you stand out from the crowd when applying for jobs during this pandemic.</li><li>The Certificate of Achievement Endorsed by the Quality Licence Scheme has helped thousands of people reach their professional goals and get better jobs.</li></ul><p><strong>Why People Enrol from One Education:</strong></p><ul><li>Receive a Quality Licence Scheme-endorsed certificate upon successful completion of the course</li><li>Learning materials of the course contain engaging voiceover and visual elements for your comfort</li><li>Get 24/7 access to all course content for a full year</li><li>Each of our students gets full tutor support on weekdays (Monday to Friday)</li><li>Efficient assessments and instant results upon completion of the course</li></ul><p>This uniquely tailored course makes online learning as efficient and easy as possible. This this course will motivate you to learn, develop, and participate in an exceptional e-learning journey while upgrading your CV &amp; competence and introducing you to new possibilities to further your career development.</p><p><strong>Course Assessment</strong></p><p>You will immediately be given access to a specifically crafted MCQ test upon completing an online module. For each test, the pass mark will be set to 60%.</p><p><strong>Certificate of Achievement</strong></p><p><strong>QLS Endorsed Certificate </strong></p><p>After completing this course, learners will get an endorsed certificate (Completly FREE) delivered right at their doorstep by post, without paying a single penny! Please note that learners outside the UK must pay a £10 postage charge.</p><p><strong>Accredited Certificate </strong></p><p>After completing the MCQ assessment of this course, you will also qualify for a CPD QS Accredited Certificate from One Education as proof of your continued professional development. Certification is available in PDF format at the cost of £9, or a hard copy can be sent to you via post at the expense of £15.</p><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This course is ideal for highly motivated individuals or teams who want to enhance their professional skills and efficiently skilled employees.</p>
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        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal entry requirements for this course, with enrollment open to anyone!</p>
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        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This course can help you to develop your career by helping you to apply for high-paying employment in the related fields.<br />Enrol now and take your career to the next step.</p>
                        </div>

Python Programming at QLS Level 7 Course

<div id="course-pills-1">Duration<p>2 Days</p><p>12 CPD hours</p>This course is intended for<p>If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.</p>Overview<p>By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data.</p><p>This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive.<br /><br />You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions.</p></div><div id="course-pills-2"><h4>Data Exploration and Cleaning</h4><ul><li>Python and the Anaconda Package Management System</li><li>Different Types of Data Science Problems</li><li>Loading the Case Study Data with Jupyter and pandas</li><li>Data Quality Assurance and Exploration</li><li>Exploring the Financial History Features in the Dataset</li><li>Activity 1: Exploring Remaining Financial Features in the Dataset</li></ul><h4>Introduction to Scikit-Learn and Model Evaluation</h4><ul><li>Introduction</li><li>Model Performance Metrics for Binary Classification</li><li>Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve</li></ul><h4>Details of Logistic Regression and Feature Exploration</h4><ul><li>Introduction</li><li>Examining the Relationships between Features and the Response</li><li>Univariate Feature Selection: What It Does and Doesn't Do</li><li>Building Cloud-Native Applications</li><li>Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients</li></ul><h4>The Bias-Variance Trade-off</h4><ul><li>Introduction</li><li>Estimating the Coefficients and Intercepts of Logistic Regression</li><li>Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters</li><li>Activity 4: Cross-Validation and Feature Engineering with the Case Study Data</li></ul><h4>Decision Trees and Random Forests</h4><ul><li>Introduction</li><li>Decision trees</li><li>Random Forests: Ensembles of Decision Trees</li><li>Activity 5: Cross-Validation Grid Search with Random Forest</li></ul><h4>Imputation of Missing Data, Financial Analysis, and Delivery to Client</h4><ul><li>Introduction</li><li>Review of Modeling Results</li><li>Dealing with Missing Data: Imputation Strategies</li><li>Activity 6: Deriving Financial Insights</li><li>Final Thoughts on Delivering the Predictive Model to the Client</li></ul></div>

Nexus Human

Predictive Analytics

Data Engineering

Big Data Analytics

Data Science

Data Mining

Data Skills

Data Science Projects with Python

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

Microsoft AZ-700

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

Python

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

Machine Learning (ML)

Machine Learning for Apps Level 4

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

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

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

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

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

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

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

Python Machine Learning Crash Course for Beginners

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

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

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

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

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

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

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

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

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

Machine Learning for Absolute Beginners - Level 2

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

TensorFlow

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

The Complete Machine Learning Course with Python

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

Learning To Learn

Machine Learning - Use in Flutter

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

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

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

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

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

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

Deep Learning

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

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