Name: Reactive Programming in Java Using RxJava 3.x: ReactiveX
Price: 33.99 GBP
Availability: InStock

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

Nexus Human

Data Engineering

Data Science

Data Skills

Trainers Course

Practical Data Science with Amazon SageMaker

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

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

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

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

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

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

                        </div>

Imperial Academy

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

Data Analyst Training

General Admission

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

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

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

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

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

Packt

Video

Web Development

Midjourney

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

Generative AI Art For Beginners

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

iStudy UK

Azure SQL Database

Azure Cosmos DB

Microsoft AZ-104

Microsoft Azure

Microsoft AZ-303

Azure Machine Learning

Azure - SQL

SkillWise

Big Data Analytics with PySpark Power BI and MongoDB

<p><strong>***24 Hour Limited Time Flash Sale***<br /><br /></strong><strong> Master Google Analytics in No Time! Admission Gifts <br /><br /></strong><strong> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter</strong></p><p>Are you a professional feeling stuck in your career, struggling to keep up with the ever-changing demands of the industry? Or perhaps you're a beginner, unsure of where to start or how to break into your desired field. Whichever stage you're in, our exclusive Learn to Master Google Analytics in No Time! Bundle provides unique insights and tools that can help you achieve your goals.</p><p>Designed to cater to the needs of both seasoned professionals and aspiring newcomers, our Learn to Master Google Analytics in No Time! bundle is a comprehensive program that will equip you with the essential skills and knowledge you need to succeed. Whether you're looking to advance in your current role or embark on a new career journey, this bundle has everything you need to take your professional life to the next level.</p><p><strong>But that's not all. When you enrol in Learn to Master Google Analytics in No Time! Online Training, you'll receive 30 CPD-Accredited PDF Certificates, Hard Copy Certificates, and our exclusive student ID card, all absolutely free.</strong></p><p><strong>Courses Are Included In this Learn to Master Google Analytics in No Time! Career Bundle:</strong></p><ul><li><strong>Course 01: </strong>Google Data Studio: Data Analytics</li><li><strong>Course 02: </strong>Diploma in Data Analysis Fundamentals</li><li><strong>Course 03: </strong>Excel Pivot Tables for Data Reporting</li><li><strong>Course 04: </strong>Basics of WordPress: Create Unlimited Websites</li><li><strong>Course 05: </strong>Receptionist Skills</li><li><strong>Course 06: </strong>Research Methods in Business</li><li><strong>Course 07: </strong>Strategic Planning and Analysis for Marketing</li><li><strong>Course 08: </strong>Create Your First Responsive Website</li><li><strong>Course 09: </strong>Quick Data Science Approach from Scratch</li><li><strong>Course 10: </strong>Business Intelligence and Data Mining</li><li><strong>Course 11: </strong>Fundamentals of Business Analysis</li><li><strong>Course 12: </strong>Data Science &amp; Machine Learning with Python</li><li><strong>Course 13: </strong>Marketing Strategies for Business</li><li><strong>Course 14: </strong>Excel Data Analysis</li><li><strong>Course 15: </strong>Statistical Analysis</li><li><strong>Course 16: </strong>Public Speaking</li><li><strong>Course 17: </strong>Minute-Taking Executive Training</li><li><strong>Course 18: </strong>Ultimate Social Media Marketing course</li><li><strong>Course 19: </strong>Microsoft Power BI - Master Power BI in 90 Minutes!</li><li><strong>Course 20: </strong>How To Do Keyword Research For SEO &amp; Ranking On Google</li><li><strong>Course 21: </strong>PowerBI Formulas</li><li><strong>Course 22: </strong>The Ultimate SEO Blueprint - Easily Ranking #1 On Google</li><li><strong>Course 23: </strong>Digital Marketing for Beginners to Advanced</li><li><strong>Course 24: </strong>Complete Communication Skills Master Class for Life</li><li><strong>Course 25: </strong>Python Course</li><li><strong>Course 26: </strong>Digital Marketing Starting an Online E-Commerce Store</li><li><strong>Course 27: </strong>Social Media Marketing-The Step by Step Guide</li><li><strong>Course 28: </strong>Complete Online Marketing &amp; Advertising Course</li><li><strong>Course 29: </strong>Learn How To Optimize Your Google My Business Page In 2016</li><li><strong>Course 30: </strong>Time Management Training - Online Course</li></ul><p>With Learn to Master Google Analytics in No Time!, you'll embark on an immersive learning experience that combines interactive lessons with voice-over audio, ensuring that you can learn from anywhere in the world, at your own pace. And with 24/7 tutor support, you'll never feel alone in your journey, whether you're a seasoned professional or a beginner.</p><p>Don't let this opportunity pass you by. Enrol in Learn to Master Google Analytics in No Time! today and take the first step towards achieving your goals and dreams.</p><p><strong>Why buy this Learn to Master Google Analytics in No Time!?</strong></p><ul><li><strong>Free CPD Accredited Certificate</strong> upon completion of Learn to Master Google Analytics in No Time!</li><li>Get <strong>a free student ID card</strong> with Learn to Master Google Analytics in No Time!</li><li><strong>Lifetime access</strong> to the Learn to Master Google Analytics in No Time! course materials</li><li>Get instant access to this Learn to Master Google Analytics in No Time! course</li><li>Learn Learn to Master Google Analytics in No Time! from anywhere in the world</li><li>24/7 tutor support with the Learn to Master Google Analytics in No Time! course.</li></ul> <p><strong>Start your learning journey straightaway with our Learn to Master Google Analytics in No Time! Training!</strong></p><p>Learn to Master Google Analytics in No Time! premium bundle consists of 30 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of Learn to Master Google Analytics in No Time! is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience.</p><p><strong>Certification</strong></p><p>You have to complete the assignment given at the end of the Learn to Master Google Analytics in No Time! course. After passing the Learn to Master Google Analytics in No Time! exam You will be entitled to claim a PDF &amp; Hardcopy certificate accredited by CPD Quality standards completely free.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        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><strong>This Learn to Master Google Analytics in No Time! course is ideal for:</strong></p><ul><li>Students seeking mastery in Learn to Master Google Analytics in No Time!</li><li>Professionals seeking to enhance Learn to Master Google Analytics in No Time! skills</li><li>Individuals looking for a Learn to Master Google Analytics in No Time!-related career.</li><li>Anyone passionate about Learn to Master Google Analytics in No Time!</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This <strong>Learn to Master Google Analytics in No Time!</strong> doesn't require prior experience and is suitable for diverse learners.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This <strong>Learn to Master Google Analytics in No Time!</strong> bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Academy for Health and Fitness

Online Course Marketing

Online Course Creation

Google Digital Marketing

Learn to Master Google Analytics in No Time!

<p><strong>***24 Hour Limited Time Flash Sale***<br /><br /></strong><strong> Software Engineering: Software Programming, Web Design &amp; Development Admission Gifts <br /><br /></strong><strong> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter</strong></p><p>Are you a professional feeling stuck in your career, struggling to keep up with the ever-changing demands of the industry? Or perhaps you're a beginner, unsure of where to start or how to break into your desired field. Whichever stage you're in, our exclusive Software Engineering: Learn All About Software's, Programming, Web Design &amp; Development Bundle provides unique insights and tools that can help you achieve your goals.</p><p>Designed to cater to the needs of both seasoned professionals and aspiring newcomers, our Software Engineering: Learn All About Software's, Programming, Web Design &amp; Development bundle is a comprehensive program that will equip you with the essential skills and knowledge you need to succeed. Whether you're looking to advance in your current role or embark on a new career journey, this bundle has everything you need to take your professional life to the next level.</p><p><strong>But that's not all. When you enrol in Software Engineering: Learn All About Software's, Programming, Web Design &amp; Development Online Training, you'll receive 30 CPD-Accredited PDF Certificates, Hard Copy Certificates, and our exclusive student ID card, all absolutely free.</strong></p><p><strong>Courses Are Included In this Software Engineering: Learn All About Software, Programming, Web Design &amp; Development Career Bundle:</strong></p><ul><li><strong>Course 01: </strong>Coding with Scratch</li><li><strong>Course 02: </strong>C# Programming - Beginner to Advanced</li><li><strong>Course 03: </strong>Python Programming Bible</li><li><strong>Course 04: </strong>Machine Learning with Python Course</li><li><strong>Course 05: </strong>Basics of WordPress: Create Unlimited Websites</li><li><strong>Course 06: </strong>Advanced Diploma in PHP Web Development with MySQL, GitHub &amp; Heroku</li><li><strong>Course 07: </strong>JavaScript Fundamentals</li><li><strong>Course 08: </strong>Node JS: API Development with Swagger</li><li><strong>Course 09: </strong>Refactor Javascript</li><li><strong>Course 10: </strong>Penetration Testing with OWASP ZAP</li><li><strong>Course 11: </strong>Advanced SAS Programming Using MacrosSQL</li><li><strong>Course 12: </strong>How To Startup Your Own SaaS (Software As a Service) Company (SaaS Evolution)</li><li><strong>Course 13: </strong>Three.js &amp; WebGL 3D Programming</li><li><strong>Course 14: </strong>Stripe with C#</li><li><strong>Course 15: </strong>C++ Development: The Complete Coding Guide</li><li><strong>Course 16: </strong>Bootstrap - Responsive Web Design</li><li><strong>Course 17: </strong>Masterclass Bootstrap 5 Course - Responsive Web Design</li><li><strong>Course 18: </strong>Diploma in User Experience UI/UX Design (Advanced)</li><li><strong>Course 19: </strong>HTML Web Development Crash Course</li><li><strong>Course 20: </strong>CSS Web Development</li><li><strong>Course 21: </strong>.NET Core API Development</li><li><strong>Course 22: </strong>Basics of WordPress: Create Unlimited Websites</li><li><strong>Course 23: </strong>The Ultimate WordPress for Beginners Step-by-Step Blueprint</li><li><strong>Course 24: </strong>Using WordPress for Affiliate Marketing</li><li><strong>Course 25: </strong>WordPress 5.0 for Beginners to Advanced</li><li><strong>Course 26: </strong>WordPress and SEO Online Course</li><li><strong>Course 27: </strong>How To Build Easy Drag &amp; Drop Landing Pages With WordPress</li><li><strong>Course 28: </strong>Make a WordPress WooCommerce Amazon Affiliate Store</li><li><strong>Course 29: </strong>WordPress Security Mastery</li><li><strong>Course 30: </strong>How To Build a WordPress Membership Site With Memberpress</li></ul><p>With Software Engineering: Learn All About Software, Programming, Web Design &amp; Development, you'll embark on an immersive learning experience that combines interactive lessons with voice-over audio, ensuring that you can learn from anywhere in the world, at your own pace. And with 24/7 tutor support, you'll never feel alone in your journey, whether you're a seasoned professional or a beginner.</p><p>Don't let this opportunity pass you by. Enrol in Software Engineering: Learn All About Software, Programming, Web Design &amp; Development today and take the first step towards achieving your goals and dreams.</p><p><strong>Why buy this Software Engineering: Learn All About Software, Programming, Web Design &amp; Development?</strong></p><ul><li><strong>Free CPD Accredited Certificate</strong> upon completion of Software Engineering: Learn All About Software, Programming, Web Design &amp; Development</li><li>Get <strong>a free student ID card</strong> with Software Engineering: Learn All About Software, Programming, Web Design &amp; Development</li><li><strong>Lifetime access</strong> to the Software Engineering: Learn All About Software, Programming, Web Design &amp; Development course materials</li><li>Get instant access to this Software Engineering: Learn All About Software, Programming, Web Design &amp; Development course</li><li>Learn Software Engineering: Learn All About Software, Programming, Web Design &amp; Development from anywhere in the world</li><li>24/7 tutor support with the Software Engineering: Learn All About Software, Programming, Web Design &amp; Development course.</li></ul> <p><strong>Start your learning journey straightaway with our Software Engineering: Learn All About Software, Programming, Web Design &amp; Development Training!</strong></p><p>Software Engineering: Learn All About Software's, Programming, Web Design &amp; Development premium bundle consists of 30 precisely chosen courses on a wide range of topics essential for anyone looking to excel in this field. Each segment of Software Engineering: Learn All About Software's, Programming, Web Design &amp; Development is meticulously designed to maximise learning and engagement, blending interactive content and audio-visual modules for a truly immersive experience.</p><p><strong>Certification</strong></p><p>You have to complete the assignment given at the end of the Software Engineering: Learn All About Software, Programming, Web Design &amp; Development course. After passing the Software Engineering: Learn All About Software's, Programming, Web Design &amp; Development exam You will be entitled to claim a PDF &amp; Hardcopy certificate accredited by CPD Quality standards completely free.</p> <h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        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>This bundle is ideal for:</p><ul><li>Students seeking mastery in this field</li><li>Professionals seeking to enhance their skills</li><li>Anyone who is passionate about this topic</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This <strong>Software Engineering: Learn All About Software, Programming, Web Design &amp; Development</strong> doesn't require prior experience and is suitable for diverse learners.</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>This <strong>Software Engineering: Learn All About Software, Programming, Web Design &amp; Development</strong> bundle will allow you to kickstart or take your career in the related sector to the next stage.</p>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

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

                        </div>

Learning & Development

Service Design Courses (SDC)

Software Engineering

Software Development

Software Engineering: Software Programming, Web Design & Development - CPD Certified

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs.</p>Overview<p>Working in a hands-on learning environment led by our expert instructor you'll:<br />Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications.<br />Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions.<br />Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications.<br />Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights.<br />Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data.<br />Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis.</p><p>Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems.</p></div><div id="course-pills-2"><h4>Introduction to Scala</h4><ul><li>Brief history and motivation</li><li>Differences between Scala and Java</li><li>Basic Scala syntax and constructs</li><li>Scala's functional programming features</li></ul><h4>Introduction to Apache Spark</h4><ul><li>Overview and history</li><li>Spark components and architecture</li><li>Spark ecosystem</li><li>Comparing Spark with other big data frameworks</li></ul><h4>Basics of Spark Programming SparkContext and SparkSession</h4><ul><li>Resilient Distributed Datasets (RDDs)</li><li>Transformations and Actions</li><li>Working with DataFrames</li></ul><h4>Spark SQL and Data Sources</h4><ul><li>Spark SQL library and its advantages</li><li>Structured and semi-structured data sources</li><li>Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.)</li><li>Data manipulation using SQL queries</li></ul><h4>Basic RDD Operations</h4><ul><li>Creating and manipulating RDDs</li><li>Common transformations and actions on RDDs</li><li>Working with key-value data</li></ul><h4>Basic DataFrame and Dataset Operations</h4><ul><li>Creating and manipulating DataFrames and Datasets</li><li>Column operations and functions</li><li>Filtering, sorting, and aggregating data</li></ul><h4>Introduction to Spark Streaming</h4><ul><li>Overview of Spark Streaming</li><li>Discretized Stream (DStream) operations</li><li>Windowed operations and stateful processing</li></ul><h4>Performance Optimization Basics</h4><ul><li>Best practices for efficient Spark code</li><li>Broadcast variables and accumulators</li><li>Monitoring Spark applications</li></ul><h4>Integrating External Libraries and Tools, Spark Streaming</h4><ul><li>Using popular external libraries, such as Hadoop and HBase</li><li>Integrating with cloud platforms: AWS, Azure, GCP</li><li>Connecting to data storage systems: HDFS, S3, Cassandra, etc.</li></ul><h4>Introduction to Machine Learning Basics</h4><ul><li>Overview of machine learning</li><li>Supervised and unsupervised learning</li><li>Common algorithms and use cases</li></ul><h4>Introduction to Spark MLlib</h4><ul><li>Overview of Spark MLlib</li><li>MLlib's algorithms and utilities</li><li>Data preparation and feature extraction</li></ul><h4>Linear Regression and Classification</h4><ul><li>Linear regression algorithm</li><li>Logistic regression for classification</li><li>Model evaluation and performance metrics</li></ul><h4>Clustering Algorithms</h4><ul><li>Overview of clustering algorithms</li><li>K-means clustering</li><li>Model evaluation and performance metrics</li></ul><h4>Collaborative Filtering and Recommendation Systems</h4><ul><li>Overview of recommendation systems</li><li>Collaborative filtering techniques</li><li>Implementing recommendations with Spark MLlib</li></ul><h4>Introduction to Graph Processing</h4><ul><li>Overview of graph processing</li><li>Use cases and applications of graph processing</li><li>Graph representations and operations</li><li>Introduction to Spark GraphX</li><li>Overview of GraphX</li><li>Creating and transforming graphs</li><li>Graph algorithms in GraphX</li></ul><h4>Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala</h4><ul><li>Overview of generative AI technologies</li><li>Integrating GPT with Spark and Scala</li><li>Practical applications and use cases Bonus Topics / Time Permitting</li></ul><h4>Introduction to Spark NLP</h4><ul><li>Overview of Spark NLP Preprocessing text data</li><li>Text classification and sentiment analysis</li></ul><h4>Putting It All Together</h4><ul><li>Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.</li></ul></div>

Pyspark

Apache Spark

Big Data Analytics

Mastering Scala with Apache Spark for the Modern Data Enterprise (TTSK7520)

<p><strong>Level 6 Diploma in Data Engineering with Google BigQuery &amp; Google Cloud</strong></p><p>Turn your skills into a competitive advantage that is recognised globally and endorsed for quality by QLS, the UK's most reputable high-quality course auditor.</p><p>Hone your skills in data analytics, big data, machine learning, data pipelines, and artificial intelligence! Create infrastructure for collecting, managing, and transforming raw data into interpretable information by data scientists and business analysts. Develop your ability to create data processing solutions, make data accessible, and evaluate and optimise organisational performance. Enrol to advance your Cloud career and show your proficiency in one of the most sought-after industries in the market.</p><p>This premium QLS endorsed Data Engineering with Google BigQuery &amp; Google Cloud course comes with intensive study modules, dedicated tutor assistance, customer support, flexible learning opportunity and a globally recognised certification all-together. Thus, successful completion of our Data Engineering with Google BigQuery &amp; Google Cloud course will give you limitless opportunities anywhere and everywhere, since the value of QLS endorsed courses are quite high, to say the least!!</p><p>What's more, the quality of our first-class course is uncompromising for both you and us. You will be taught the richest of materials from this exquisite Data Engineering with Google BigQuery &amp; Google Cloud course, enabling you to open up a vast world of opportunities for you to grasp and gain success from. For the price point, this course is actually an absolute steal! It's an investment you absolutely do not want to miss out on!</p><p>So, why delay any longer? Benefit your career progression by joining us today and distinguish yourself as, unquestionably, the most skilled one in your workplace!</p><p><strong>Learning Outcomes for Level 6 Diploma in Data Engineering with Google BigQuery &amp; Google Cloud:</strong></p><ol><li>Develop a comprehensive understanding of data engineering principles and concepts.</li><li>Utilise Google BigQuery and Google Cloud for efficient data warehousing and analysis.</li><li>Explore and manipulate data effectively using BigQuery for insightful data-driven decisions.</li><li>Apply data flow techniques to design and optimise data pipelines.</li><li>Implement virtual data solutions using views for efficient data organisation and retrieval.</li><li>Create visually appealing and informative data visualisations to communicate insights effectively.</li></ol><p><strong>Why Learn with us: </strong></p><p>We, as one of the leading e-learning service providers across the globe, strive to provide all our learners with the best e-learning experience possible that can make a real difference in their career progression.</p><p><strong>Our Service for the </strong><strong>Data Engineering with Google BigQuery &amp; Google Cloud</strong><strong> Includes:</strong></p><ul><li>Intensive Study Notes</li><li>Tutor Support</li><li>Customer Support</li><li>Assignment Help</li><li>QLS Endorsed Certificate</li><li>24/7 Learning Portal Access</li><li>Widely Compatible Study Materials</li></ul><p><strong>Course Curriculum of </strong><strong>Data Engineering with Google BigQuery &amp; Google Cloud</strong></p><ul><li><strong>Unit 01: </strong>Introduction<strong><br /></strong></li><li><strong>Unit 02: </strong>Introducing Data Warehouse &amp; BigQuery<strong><br /></strong></li><li><strong>Unit 03: </strong>First Exploration<strong><br /></strong></li><li><strong>Unit 04: </strong>Data Flow Basic<strong><br /></strong></li><li><strong>Unit 05: </strong>Diving into BigQuery<strong><br /></strong></li><li><strong>Unit 06: </strong>Virtual Data using View<strong><br /></strong></li><li><strong>Unit 07: </strong>Data Visualisation<strong><br /></strong></li><li><strong>Unit 08: </strong>Intermediate Query</li></ul><p><strong>Assessment &amp; Certification</strong></p><p>Learners must submit a comprehensive summerised assignment on all the units that will be assessed by our expert tutor if all the learning outcomes are met as per the standard set by QLS. After the quality check, learner will be provided with the Data Engineering with Google BigQuery &amp; Google Cloud certificate.</p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme.</strong></p><p>An endorsed certificate will be issued to learners at the end of the course as recognition of course completion. Provided that learner completes all the assessment of a course can claim for certificate.</p><p><strong>Endorsement:</strong></p><p>QLS endorsed this certified course as a highly qualified, non-regulated provision and training programme. There will be a trainer to answer all your questions including your progression routes into further higher education.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>Our course is open for everyone. This Data Engineering with Google BigQuery &amp; Google Cloud is ideal for those who aspire to gain in-depth knowledge &amp; efficient skill to embark into the relevant sector as a promising potential.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>There are no formal requirements needed for this Data Engineering with Google BigQuery &amp; Google Cloud course. So learners do not require any prior qualifications to enrol in this course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p><strong>Here are few job roles related to Data Engineering with Google BigQuery &amp; Google Cloud </strong>(Salary-per annum):</p><ol><li>Data Engineer (£50,000 - £55,000)</li><li>Data Analyst: (£38,000 - £40,000)</li><li>Data Scientist (£40,000 - £70,000)</li><li>Data Architect (£80,000 - £90,000)</li><li>Cloud Data Engineer (£50,000 - £80,000)</li><li>Big Data Engineer (£50,000 - £80,000)</li></ol>
                        </div>

Kingston Open College

ELS (Effective Learning Service)

ICDL (International Certification of Digital Literacy)

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

Level 6 Diploma in Data Engineering with Google BigQuery & Google Cloud - QLS Endorsed

<strong>Build next-gen programming skills using RxJava 3.0 and Reactive Streams</strong>
        <p>In this course, you'll learn about Reactive programming and libraries such as RxJava and Reactor that are designed to keep the application responsive and make the system more resilient.
The course starts by helping you understand the fundamentals of RxJava to make it easy for you to learn advanced topics such as the Reactive Manifesto, callbacks, callback hell, sync vs async, concurrent vs parallel, and the observer design pattern. As you advance, you'll also dive into the concepts of observable and observers, operators, combining observables, and replaying caching and subjects. You'll then explore concurrency and parallelism and get to grips with buffering, throttling, and switching. 
By the end of the course, you'll have developed a solid understanding of Reactive programming concepts and RxJAVA.

All the code and supporting files for this course are available here: https://github.com/PacktPublishing/Reactive-Programming-in-Java-Using-RxJava-3.x-ReactiveX</p>
        <h2>What You Will Learn</h2><p>Get to grips with Reactive programming fundamentals<br>Explore RxJava 3 and its features<br>Understand the implementation of Java Reactive Streams<br>Learn about the observer design pattern<br>Explore the different Streams in Java with the help of coding examples</p>
        <h2>Audience</h2><p>If you want to learn RxJava and Reactive programming and use them to create amazing applications, this course is for you.</p>
        <h2>Approach</h2><p>A hands-on course that will help you learn the concepts of RxJava 3.0 and Reactive Streams with the help of coding examples.</p>
        <h2>Key Features</h2><p>Become proficient in Reactive programming using RXJava 3.x * Explore different Reactive Streams and learn about their implementation * Understand how different operators and observables work in Java</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/reactive-programming-in-java-using-rxjava-3.x-reactivex</p>
        <h2>About the Author</h2><h3>Basics Strong</h3><p>Basics Strong is a team of technocrats from IITs who focus on solving problems using technology. They work on mission-critical projects in AI, machine learning, and BlockChain as a domain and use Java, Python, JavaScript, and a lot of tools and technologies. They love to code and program.

The team believes that a strong foundation in the basics of programming concepts can help you solve any technical problem and excel in your career. Therefore, they create courses that help you build your basics and come up with ways to make complicated concepts easy to learn. All their courses are carefully crafted to include hands-on examples and comprehensive working files for practical learning.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Reactive Programming Introduction</h4><p></p><br><h4>3. Hello RxJava!</h4><p></p><br><h4>4. The Observable And Observers</h4><p></p><br><h4>5. Operators</h4><p></p><br><h4>6. Combining Observables</h4><p></p><br><h4>7. Subjects, Replaying And Caching : Ways To Multicast</h4><p></p><br><h4>8. Concurrency And Parallelization</h4><p></p><br><h4>9. Buffering, Throttling, Switching</h4><p></p><br><h4>10. Flowable and Backpressure</h4><p></p><br><h4>11. Streams and Parallel Streams</h4><p></p>

Java

Programming

Build next-gen programming skills using RxJava 3.0 and Reactive Streams

Reactive Programming in Java Using RxJava 3.x: ReactiveX

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

Microsoft AZ-900

Microsoft AZ-700

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

Python

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

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<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.

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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>
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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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