<strong>If you are someone who wants to build a modern responsive website, then you are at the right place. Learn to create a responsive website with Bootstrap concepts and advance your website development skills to the next level with thoroughly explained lessons and many examples displayed on screen size for thorough understanding.</strong>
        <p>Bootstrap is a free, open-source, front-end development framework to create websites and web applications. Bootstrap is designed for the responsive development of mobile-first websites and provides a collection of syntax for template designing. It contains HTML, CSS, and JavaScript-based design templates for typography, forms, buttons, navigation, and other interface components.

This course begins with an introduction to the concepts and components of Bootstrap and demonstrates how to set up the software to run the first example on a web page. The course then advances to teaching about Bootstrap lists, collapsible content, dropdowns, forms, and modals. The course provides a solid understanding of the Bootstrap framework with the goal to create a modern responsive website. You will also learn to code a basic to advanced website with the software.

Upon completion, the course will provide a comprehensive understanding of Bootstrap concepts and a solid understanding of the Bootstrap framework. You can hone your HTML and CSS skills for responsive web development and learn what a responsive website is and how to create one.

All resources are available at: https://github.com/PacktPublishing/The-Complete-Bootstrap-Beginners-Course-with-100-examples</p>
        <h2>What You Will Learn</h2><p>Build your own complex website with ease<br>Garner thorough knowledge of Bootstrap, CSS, and HTML<br>Explore how the components of Bootstrap work<br>Understand the concepts of a framework comprehensively<br>Learn web development using CSS styles for designing<br>Learn to write advanced codes using Bootstrap</p>
        <h2>Audience</h2><p>This course has been designed for anyone wishing to learn Bootstrap from scratch and looking for an easy way to understand Bootstrap concepts and components. Beginners looking to learn about a responsive website and how to create one, and those looking to learn the fundamentals of HTML, CSS, and Bootstrap, will benefit from this course. The course prerequisites include a fair understanding of basic HTML and CSS and the fundamental mechanics of web development. You must aspire to create a modern responsive website and be knowledgeable in using a code editor such as Notepad++, Brackets, and others.<br></p>
        <h2>Approach</h2><p>The course delivery mechanism includes video lectures that help you thoroughly understand the concepts of Bootstrap, its concepts, and its components. Frequent recaps and refreshers with hands-on coding exercises and quizzes for each lesson will make learning easily quantifiable and lucidly understandable.</p>
        <h2>Key Features</h2><p>Gain comprehensive understanding of basic HTML and CSS and mechanics of web development * Experience hands-on exercises to grasp responsive web designing on Bootstrap * Master the concepts with live code thoroughly explained with examples displaying screen sizes</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/The-Complete-Bootstrap-Beginners-Course-with-100-examples</p>
        <h2>About the Author</h2><h3>Amit Diwan</h3><p>Studyopedia was founded by Amit Diwan in 2018 after working for Tutorialspoint , IIT, IASRI, Sitepoint, DU, and C# Corner. Studyopedia sells courses on Udemy, Tutorialspoint, Geeksforgeeks, and Skillshare, providing video courses to master various technologies and programming languages, databases, frameworks, Python, data science, machine learning, Java, Android, C/C++, HTML5, Bootstrap, JavaScript, jQuery, PHP, CSS, WordPress, Drupal, Joomla, Magento, osCommerce, OpenCart, PrestaShop, and other disciplines. Studyopedia delivers high-quality video courses to millions of students and professionals enrolled through their website on multiple programming languages and technologies.</p>
        <h2>Course Outline</h2><h4>1. Bootstrap Introduction</h4><p></p><br><h4>2. Bootstrap Setup and first example</h4><p></p><br><h4>3. Bootstrap Grids - Create a Web Page Layout</h4><p></p><br><h4>4. Create Buttons on a Web Page</h4><p></p><br><h4>5. Create Tables on a Web Page</h4><p></p><br><h4>6. Change Colors on a Web Page</h4><p></p><br><h4>7. Create Alerts</h4><p></p><br><h4>8. Create Badges</h4><p></p><br><h4>9. Create Progress Bars</h4><p></p><br><h4>10. Set Navigation on a Web Page</h4><p></p><br><h4>11. Images on a web page</h4><p></p><br><h4>12. Bootstrap Components</h4><p></p><br><h4>13. Collapsible content - Show, hide and organize content</h4><p></p><br><h4>14. Dropdown in Bootstrap</h4><p></p><br><h4>15. Create Forms on a Web Page</h4><p></p><br><h4>16. Create List Groups</h4><p></p><br><h4>17. Create a Basic Modal on a web page</h4><p></p>

Packt

Web Development

HTML

Bootstrap

If you are someone who wants to build a modern responsive website, then you are at the right place. Learn to create a responsive website with Bootstrap concepts and advance your website development skills to the next level with thoroughly explained lessons and many examples displayed on screen size for thorough understanding.

The Complete Bootstrap Beginners Course with 100+ examples

General Admission

<strong>In this compact intermediate-level course, you will learn how to use Facebook Prophet to do time series analysis and forecasting. You will learn how Prophet works under the hood and the Prophet API. We will apply Prophet to a variety of datasets, including store sales and stock prices.</strong>
        <p>Prophet enables Python and R developers to build scalable time series forecasts. This course will help you implement Prophet's cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code.

In this course, you will learn how to use Facebook Prophet to do time series analysis and forecasting. You will learn how the Prophet works under the hood (that is, what are its modeling assumptions?) and the Prophet API (that is, how to write the code).

This course is a practice-oriented course, demonstrating how to prepare your data for Prophet, fit a model, and use it to forecast, analyze the results, and evaluate the model's predictions. We will apply Prophet to a variety of datasets, including store sales and stock prices. You will learn how to use Prophet to plot the model's in-sample predictions and forecast. Then, learn how to plot the components of the fitted model. You will also learn how to deal with outliers, missing data, and non-daily (for example, monthly) data.

By the end of this course, you will be able to use Prophet confidently to forecast your data.</p>
        <h2>What You Will Learn</h2><p>Prepare your data (a Pandas dataframe) for Facebook Prophet<br>Learn how to fit a Prophet model to a time series<br>Plot the components of the fitted model<br>Model holidays and exogenous regressors<br>Evaluate your model with forecasting metrics<br>Learn how to do changepoint detection with Prophet</p>
        <h2>Audience</h2><p>Anyone interested in data science, machine learning, or who wishes to use time series analysis on their own data should take this course. Good Python programming skills are required, as well as knowledge of Pandas, Dataframes, and preferably some familiarity with Scikit-Learn, though this is not required.</p>
        <h2>Approach</h2><p>This course is a learn-by-doing course where you will learn how to prepare your data for Prophet, fit a model, and use it to forecast, analyze the results, and evaluate the model's predictions. The course is well-balanced with both theoretical and practical coding exercises. In each section, we first cover the theory concept and demonstrate it using a real-world example for better understanding.</p>
        <h2>Key Features</h2><p>Teaches how to make a forecast using Prophet * Explains how to use Prophet to predict stock prices * Covers how to use Prophet to plot the model's in-sample predictions and forecast</p>
        
        <h2>About the Author</h2><h3>Lazy Programmer</h3><p>The Lazy Programmer is an AI/ML engineer focusing on deep learning with experience in data science, big data engineering, and full-stack development. With a background in computer engineering and specialization in ML, he holds two master's degrees in computer engineering and statistics with finance applications. His online advertising and digital media expertise include data science and big data.

He has created DL models for prediction and has experience in recommender systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He is a web programmer with experience in Python, Ruby/Rails, PHP, and Angular.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p>Welcome to the course! In this section, we will get introduced to the course goals.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will introduce Facebook Prophet and understand the course learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Outline</p><p style='margin: 0'>In this video, we will understand the course learning approach and what is required to start with this course. Then we will also understand what is covered in this course.</p></td></tr></tbody></table><br><h4>2. Time Series Basics</h4><p>In this section, we will talk about time series basics.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Time Series Basics Section Introduction</p><p style='margin: 0'>In this video, we will get introduced to time series and understand some basics.</p></td></tr><tr><td style='padding: 15px;'><p>2. Forecasting Metrics</p><p style='margin: 0'>In this video, we will understand how to forecast metrics.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Naive Forecast and the Importance of Baselines</p><p style='margin: 0'>In this video, we will discuss baselines and naïve forecast.</p></td></tr><tr><td style='padding: 15px;'><p>4. Walk-Forward Validation</p><p style='margin: 0'>In this video, we will discuss the concept of walk-forward validation.</p></td></tr><tr><td style='padding: 15px;'><p>5. Suggestion Box</p><p style='margin: 0'>In this video, we will take a look at the suggestion box where we can add feedback for this course.</p></td></tr></tbody></table><br><h4>3. Facebook Prophet</h4><p>In this section, we will explore Facebook Prophet.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How Does Prophet Work?</p><p style='margin: 0'>In this video, we will understand concepts behind Facebook Prophet and understand how it works.</p></td></tr><tr><td style='padding: 15px;'><p>2. Prophet: Code Preparation</p><p style='margin: 0'>In this video, we will go through the code structure for Prophet.</p></td></tr><tr><td style='padding: 15px;'><p>3. Prophet in Code: Data Preparation</p><p style='margin: 0'>In this video, we will work on data preparation with Prophet.</p></td></tr><tr><td style='padding: 15px;'><p>4. Prophet in Code: Fit, Forecast, Plot</p><p style='margin: 0'>In this video, you will learn how to fit, forecast, and plot our data.</p></td></tr><tr><td style='padding: 15px;'><p>5. Prophet in Code: Holidays and Exogenous Regressors</p><p style='margin: 0'>In this video, you will learn how to add holidays and exogenous regressors to our data.</p></td></tr><tr><td style='padding: 15px;'><p>6. Prophet in Code: Cross-Validation</p><p style='margin: 0'>In this video, you will learn how to do cross-validation with Prophet.</p></td></tr><tr><td style='padding: 15px;'><p>7. Prophet in Code: Changepoint Detection</p><p style='margin: 0'>In this video, you will learn how to detect changepoint with Prophet.</p></td></tr><tr><td style='padding: 15px;'><p>8. Prophet: Multiplicative Seasonality, Outliers, Non-Daily Data</p><p style='margin: 0'>In this video, we will work on multiplicative seasonality, outliers, and non-daily data with Prophet.</p></td></tr><tr><td style='padding: 15px;'><p>9. (The Dangers of) Prophet for Stock Price Prediction</p><p style='margin: 0'>In this video, we will work with Prophet for stock price prediction.</p></td></tr><tr><td style='padding: 15px;'><p>10. Prophet Section Summary</p><p style='margin: 0'>In this video, we will summarize what we have learnt in this section.</p></td></tr></tbody></table>

Data Science

Python

In this compact intermediate-level course, you will learn how to use Facebook Prophet to do time series analysis and forecasting. You will learn how Prophet works under the hood and the Prophet API. We will apply Prophet to a variety of datasets, including store sales and stock prices.

Data Science - Time Series Forecasting with Facebook Prophet in Python

Scala

Big Data

Scala is doubtless one of the most in-demand skills for data scientists and data engineers. This competitive course will teach you the essential concepts and methodologies of Scala with a lot of practical implementations.

Scala & Spark-Master Big Data with Scala and Spark

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					<div><p>Python is a general-purpose programming language that is widely used in data science. It is known for its simplicity, readability, and versatility. In this Python, JavaScript, and Microsoft SQL for Data science course, you will learn the basics of Python, including variables, data types, operators, conditional statements, control flow statements, functions and error handling. You will also learn how to use Python for data science tasks such as cleaning, analysis and visualization.</p><p>JavaScript is a scripting language. It is primarily used for front-end web development. However, it also can be used for data science tasks such as data visualization and data analysis.</p><p>In this course, you will learn the basics of JavaScript, including variables, data types, operators, conditional statements, control flow statements, functions, and error handling. You will also learn how to use JavaScript for data science tasks such as data cleaning and data visualization.</p><p>Microsoft SQL is a relational database management system (RDBMS) that stores, manages, and retrieves data. It is one of the most popular RDBMSs in the world.</p><p>In this course, you will learn the basics of Microsoft SQL, including SQL statements, data manipulation statements, data query statements, and data definition statements. You will also learn to use Microsoft SQL to create, read, update, and delete data from a database.</p></div>
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										<span>Understand the foundational principles of JavaScript, Python, and Microsoft SQL.</span>
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										<span>Use Python for data science tasks such as data cleaning, analysis, and visualization</span>
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										<span>Query and manage data using Microsoft SQL</span>
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										<span>Develop and design data science solutions</span>
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			<h2> <b>Why should I take this course?</b></h2>		</div>
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										<span>Comprehensive coverage of three primary data science languages.</span>
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										<span>Tailored curriculum to ensure step-by-step progression.</span>
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										<span>Future-proof your skills in an ever-evolving tech landscape.</span>
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										<span>Position yourself as a versatile coder in the job market.</span>
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										<span>Transform raw data into insightful conclusions effectively.</span>
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										<span>Access to a computer with an internet connection</span>
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										<span>A willingness to learn and practice</span>
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Skill Up

Computing Skills

Data Engineering

Microsoft SQL

Data Skills

Microsoft AZ-303

Become a data scientist with the in-demand skills of Python, JavaScript, and Microsoft SQL. This comprehensive course will teach you the essential skills you need to succeed.

Learn Python, JavaScript, and Microsoft SQL for Data science

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					<div><p>Python is one of the most popular programming languages in the world, and for good reason. It's versatile, powerful, and easy to learn. With this course, you'll gain a deep understanding of all aspects of Python programming, from the basics of variables and functions to more advanced topics like object-oriented programming, regular expressions, and databases.</p><p>Starting with the basics, we'll lay a solid foundation in 'Introduction &amp; Setup' before venturing into the realms of 'Classes/Objects' and 'Regular Expressions.' Here, you'll learn how to harness the power of Python's object-oriented programming capabilities and create complex patterns that match and process text in ways you never thought possible. As we delve deeper, you'll discover the magic of CGI Programming, unlocking the secrets to crafting web-based applications that truly stand out. And, of course, no Python course would be complete without a deep dive into 'Database Management' and 'Multithreading,' ensuring you're well-equipped to handle the challenges of modern-day programming.</p><p>But that's not all. Our course also includes dedicated sections on 'Networking' and 'GUI,' where you'll learn how to create beautiful, user-friendly interfaces and connect your applications to the world. By the time you reach the final section on 'Resources,' you'll have gained not just knowledge, but a valuable skill set that will open up countless opportunities and give you the confidence to tackle any programming challenge that comes your way. So, what are you waiting for? Join us on this exciting journey and unlock the full potential of your programming abilities!</p></div>
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										<span>Manage and interact with databases using Python.</span>
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			<h2> <b>Why should I take this course?</b></h2>		</div>
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										<span>Comprehensive coverage of Python programming.</span>
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										<span>Skills applicable to a wide range of programming tasks.</span>
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										<span>Flexibility to learn at your own pace.</span>
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										<span>Expertly crafted content for effective learning.</span>
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										<span>Future-proof your career with relevant programming skills.</span>
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										<span>Data Analyst</span>
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										<span>Systems Administrator</span>
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										<span>Machine Learning Engineer</span>
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										<span>Network Engineer</span>
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			<h2> <b>Requirements</b></h2>		</div>
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										<span>Basic understanding of programming concepts.</span>
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										<span>A computer with Python installed.</span>
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										<span>An internet connection for accessing course resources.</span>
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Turn your coding dreams into reality with the Python Programming Bible | Networking, GUI, Email, XML, CGI.

Python Programming Bible | Networking, GUI, Email, XML, CGI

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					<div><p>Welcome to our high-quality Advanced Google Ads / AdWords Training! This course is made for all - from newbies to pro marketers. You're going to master Google Ads in this course.</p><p>First up, we're exploring Ad concepts. You'll see how paid Google Ads and organic search results play together. You'll learn from the Paid and Organic Interaction Report. Next, we'll dive into Geolocal Campaigns. These are ads aimed at folks based on where they are. You'll learn a two-campaign method for the best results.</p><p>Next, we're checking out experiments in Google Ads. You'll learn to set budgets for lots of ad accounts automatically. We'll also look at how to check AdWords and create solid remarketing plans. You'll get to see how AI &amp; Machine Learning can make your ads better.</p><p>As we near the end, we'll get into dynamic ads, Google Analytics, and scripts. We'll look at tools for studying your competitors and software for making landing pages. And finally, we'll get you up to speed on the latest Google Ads features. This way, you're always in the know about the newest trends in Google Ads.</p><p>This version should be more readable and easier to understand for a wider audience while still maintaining the necessary depth of information.</p></div>
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			<h2><b> Learning outcome</b></h2>		</div>
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										<span>Gain a comprehensive understanding of Google Ads.</span>
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										<span>Learn how to conduct AdWords audits and remarketing strategies.</span>
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										<span>Master advanced Google Ads features like automation and AI.</span>
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										<span>Get proficient with tools like AdWords Editor and Google Analytics.</span>
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										<span>Develop a deep understanding of competitor research tools and landing page software.</span>
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			<h2> <b>Why should I take this course?</b></h2>		</div>
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										<span>Acquire skills to leverage Google Ads for business marketing.</span>
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										<span>Enhance your career prospects in digital marketing.</span>
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										<span>Understand how to effectively audit and strategize AdWords campaigns.</span>
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										<span>Develop a skillset to boost your business's online visibility.</span>
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										<span>Obtain a certificate to validate your expertise in Google Ads.</span>
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			<h2> <b>Career Path</b></h2>		</div>
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										<span>Google Ads Specialist</span>
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										<span>Digital Marketing Strategist</span>
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										<span>Search Engine Marketer</span>
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										<span>Digital Marketing Manager</span>
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										<span>AdWords Consultant</span>
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			<h2> <b>Requirements</b></h2>		</div>
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										<span>Basic knowledge of digital marketing.</span>
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										<span>Willingness to learn advanced marketing techniques.</span>
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										<span>A reliable internet connection for online learning.</span>
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Master Google Ads strategies to boost your business. Learn advanced concepts, testing, auditing, automation, and AI. Begin now!

Advanced Google Ads / AdWords Training

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			<h2>Learning Outcomes
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										<span>Learn what Python programming is and its basic data types</span>
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										<span>Know the fundamentals of Python operators</span>
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										<span>Deepen your understanding of control flow part - 1 and control flow part - 2</span>
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										<span>Enhance your idea of user input and error handling</span>
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										<span>Get acquainted with Python scripting and libraries</span>
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										<span>Get an introduction to OPP and learn the advanced OPP</span>
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			<h2>Description </h2>		</div>
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				<p><span>According to a statistic, Python is one of the most demanding jobs in the UK, which offers a salary of almost </span><span>£60,000 to programmers who are skilled in Pythons. This demand opens up many opportunities for freshers and experts in various IT fields. So, if you are from a programming background or wish to start your career in the relevant field, gain adequate skills in Python and shine in your future career with the help of the Python Programming: Beginner to Experts course.</span></p><p><span>Our immersive Python Programming: Beginner to Experts course will help you gain a thorough understanding of what Python is all about and in which sectors it's mostly used. Moreover, this course will provide you with knowledge of basic data types. In addition, you will learn about Python operators, user input and error handling and many more. While progressing through your study, you can also get to learn about Python scripting and libraries. It will also introduce you to the primary and advanced levels of OPP.</span></p><p><span>However, after completing this comprehensive course, you will immediately receive a CPD- Certificate of achievement, which will not only help you boost your confidence but also put some weight on your resume. So, enrol and begin your journey now!</span></p>					</div>
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			<h2>Certificate of Achievement </h2>		</div>
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				<p><span>After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post. </span></p>					</div>
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			<h2>Method of Assessment 
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				<p><span>After completing this course, you will be provided with some assessment questions. To pass that assessment, you need to score at least 60%. Our experts will check your assessment and give you feedback accordingly.</span></p>					</div>
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			<h2>Career path</h2>		</div>
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				<p><span>We designed this course with the aim of increasing your expertise in Python so that you can pursue your career in the relevant field. Such as - </span></p>					</div>
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										<span>Web Developer</span>
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										<span>Software Engineer</span>
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										<span>Data Scientist</span>
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										<span>Machine Learning Engineer</span>
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										<span>Data Analyst</span>
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Janets

Python Programming: Beginner To Expert

<p><strong>***24 Hour Limited Time Flash Sale***<br /><br /> Master GIS: ArcGIS for Hydrology, Spatial Analysis, Remote Sensing &amp; Cartography Admission Gifts <br /><br /> FREE PDF &amp; Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter</strong></p><p>Have you ever dreamed of turning your love for maps and data into a fulfilling career? The geospatial science field is booming in the UK, but a recent study by the Chartered Institute of Environmental Management revealed a shocking fact: <strong>72%</strong> of environmental consultancies are struggling to find qualified professionals with the right GIS and data analysis skills. That's where our Master GIS program comes in!</p><p>This Master GIS program provides in-depth training on ArcGIS, the industry-standard GIS software. You'll master essential skills like spatial analysis, remote sensing for data collection, and creating professional maps using both ArcGIS and open-source QGIS. Additionally, you'll gain expertise in Python programming, data visualisation, and web mapping, making you a highly sought-after geospatial professional.</p><p><strong>With this Master GIS: ArcGIS for Hydrology, Spatial Analysis, Remote Sensing &amp; Cartography bundle, you will get 20 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 Master GIS: ArcGIS for Hydrology, Spatial Analysis, Remote Sensing &amp; Cartography Career Bundle:</strong></p><ul><li><strong> Course 01: </strong>Diploma in ArcGIS for Hydrology at QLS Level 4</li><li><strong> Course 02:</strong> Award in Spatial Analysis in ArcGIS at QLS Level 2</li><li><strong> Course 03:</strong> Remote Sensing in ArcGIS</li><li><strong> Course 04:</strong> QGIS Cartography</li><li><strong> Course 05:</strong> Business Intelligence and Data Mining Diploma</li><li><strong> Course 06:</strong> Python Data Science</li><li><strong> Course 07: </strong>Spatial Data Visualization and Machine Learning in Python Level 4</li><li><strong> Course 08</strong>: Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4</li><li><strong> Course 09: </strong>Maps in R Shiny and Leaflet</li><li><strong> Course 10:</strong> Create Smart Maps in Python and Leaflet Level 3</li><li><strong> Course 11: </strong>Advanced Diploma in Geology</li><li><strong> Course 12: </strong>Structural Geology, Tectonics &amp; Rock Mechanics</li><li><strong> Course 13:</strong> Geospatial Technology, Remote Sensing and Web Mapping</li><li><strong> Course 14:</strong> Environmental Management</li><li><strong> Course 15:</strong> Environmental Law</li><li><strong> Course 16:</strong> Carbon Literacy Project</li><li><strong> Course 17:</strong> Forensic Anthropology</li><li><strong> Course 18:</strong> Autocad Design</li><li><strong> Course 19:</strong> Meteorology</li><li><strong> Course 20: </strong>Architectural Studies</li></ul><p>Invest in Your Future: Whether you're looking to launch a new career in GIS or enhance your existing skillset, this course bundle is your perfect starting point. Take your first step towards a rewarding career - enrol today!</p><p><strong>Learning Outcomes of Master GIS: ArcGIS for Hydrology</strong></p><ul><li>Apply advanced ArcGIS techniques for hydrological analysis.</li><li>Conduct spatial analysis to uncover hidden patterns in geospatial data.</li><li>Utilise remote sensing imagery to extract valuable information.</li><li>Create professional and informative maps using ArcGIS and QGIS.</li><li>Analyse and visualise spatial data using Python libraries.</li><li>Develop interactive web maps using Python and Leaflet.</li></ul><p><strong>Why Choose Us?</strong></p><ul><li>Get a <strong>Free CPD Accredited Certificate</strong> upon completion of Master GIS: ArcGIS for Hydrology</li><li>Get a <strong>free student ID card</strong> with Master GIS: ArcGIS for Hydrology Training program (£10 postal charge will be applicable for international delivery)</li><li>The Master GIS: ArcGIS for Hydrology is <strong>affordable and simple</strong> to understand</li><li>This course is <strong>entirely online, interactive lesson</strong> with voiceover audio</li><li>Get <strong>Lifetime access</strong> to the Master GIS: ArcGIS for Hydrology course materials</li><li>The Master GIS: ArcGIS for Hydrology comes with <strong>24/7 tutor support</strong></li></ul> <p>Take a step toward a brighter future!</p><p><strong>*** Course Curriculum ***</strong></p><p><strong> Course 01: Diploma in ArcGIS for Hydrology at QLS Level 4</strong></p><p>Section 01: Introduction To ArcGIS Software</p><ul><li>Introduction to ArcGIS software</li><li>Selection by Location, attributes, clip features and tables to Excel</li><li>Performing actions on the data: change the projections, dissolve, clip</li><li>From .TXT and .DXF to shapefile</li><li>Calculations with attribute table and KML files in ArcMap</li><li>Export from ArcMap to PDF</li><li>ArcScene 3D</li><li>GIS Example Test</li><li>AutoCAD fixing polygon coordinates</li></ul><p>Section 02: ArcGIS For Hydrology</p><ul><li>Download hydrological data</li><li>Calculate flow direction and flow accumulation</li><li>Watershed delineation</li><li>Clip layers to watershed boundary</li><li>Stream segments and catchments areas</li><li>Stream order convert to vector</li><li>Spatial join to catchments</li><li>Join stream id to stream order</li><li>Map data and initial layout</li><li>Add and format map elements</li></ul><p><strong> Course 02: Award in Spatial Analysis in ArcGIS at QLS Level 2</strong></p><ul><li>Module 01: Point Density Analysis</li><li>Module 02: Raster Calculator and Vector Isolation</li><li>Module 03: Raster to Topography</li><li>Module 04: Raster Reclassification</li><li>Module 05: Raster Overlay</li><li>Module 06: Slope Analysis and Hydrology tools</li><li>Module 07: Introduction to TIFF Files</li><li>Module 08: Introduction to 3D Surfaces</li><li>Module 09: Satellite Images and TIN Surfaces</li><li>Module 10: Exercise</li></ul><p><strong> Course 03: Remote Sensing in ArcGIS</strong></p><ul><li>Module 01: Remote sensing, satellite images, spectral bands introduction</li><li>Module 02: Layers stacking satellite images</li><li>Module 03: Georeferencing satellite images</li><li>Module 04: Introduction to geoprocessing raster tools</li><li>Module 05: Raster Analysis Functions</li><li>Module 06: Georeferencing toposheet</li><li>Module 07: Site suitability using weighted overlay analysis - part 1</li><li>Module 08 Site suitability using weighted overlay analysis - part 2</li><li>Module 09: Watershed Delineation from DEM</li><li>Module 10: Unsupervised classification</li></ul><p><strong>=========&gt;&gt;&gt;&gt;&gt; And 17 More Courses &lt;&lt;&lt;&lt;&lt;=========</strong></p><p><strong>How will I get my Certificate?</strong></p><p>After successfully completing the course, you will be able to order your Certificates as proof of your achievement.</p><ul><li><strong>PDF Certificate: </strong><strong>Free </strong>(Previously it was £12.99*20 = £259)</li><li><strong>CPD Hard Copy Certificate: </strong>Free ( For The First Course: Previously it was £29.99)</li><li><strong>QLS Endorsed Hard Copy Certificate: </strong>£79)</li></ul> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>Anyone interested in learning more about the topic is advised to take this bundle. This bundle is ideal for:</p><ul><li>GIS enthusiasts</li><li>Environmental scientists</li><li>Urban planners</li><li>Data analysts</li><li>Geographers</li></ul>
                        </div> <h2>
        
        <div>Requirements</div>
    </h2>

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

                        <div id="career_path">
                            <p>After completing this course, you are to start your career or begin the next phase of your career.</p><ul><li>GIS Analyst</li><li>Cartographer</li><li>Environmental Consultant</li><li>Data Scientist</li><li>Web Developer</li><li>Planner</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Accredited Digital Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p>Upon passing the Course, you need to order a <strong>Digital Certificate</strong> for each of the courses inside this bundle as proof of your new skills that are accredited by <strong>CPD QS </strong>for Free.</p></div>
        <h4>CPD Accredited Hard Copy Certificate</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div><p><em>Please note that International students have to pay an additional £10 as a shipment fee.</em></p></div>
        <h4>Award in Spatial Analysis in ArcGIS at QLS Level 2</h4>
        <p>
            Hard copy certificate -
            £79
        </p>
            <div><p><em>Please note that International students have to pay an additional £10 as a shipment fee.</em></p></div>

                        </div>

Academy for Health and Fitness

Master GIS: ArcGIS for Hydrology, Spatial Analysis, Remote Sensing & Cartography

<div id="course-pills-1">Duration<p>3 Days</p><p>18 CPD hours</p>This course is intended for<p>This course is geared for Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.</p>Overview<p>Working in a hands-on learning environment, guided by our expert team, attendees will learn to:<br />Understand how data analysts and scientists gather and analyze data<br />Perform data analysis and data wrangling using Python<br />Combine, group, and aggregate data from multiple sources<br />Create data visualizations with pandas, matplotlib, and seaborn<br />Apply machine learning (ML) algorithms to identify patterns and make predictions<br />Use Python data science libraries to analyze real-world datasets<br />Use pandas to solve common data representation and analysis problems<br />Build Python scripts, modules, and packages for reusable analysis code<br />Perform efficient data analysis and manipulation tasks using pandas<br />Apply pandas to different real-world domains with the help of step-by-step demonstrations<br />Get accustomed to using pandas as an effective data exploration tool.</p><p>Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.</p></div><div id="course-pills-2"><h4>Introduction to Data Analysis</h4><ul><li>Fundamentals of data analysis</li><li>Statistical foundations</li><li>Setting up a virtual environment</li></ul><h4>Working with Pandas DataFrames</h4><ul><li>Pandas data structures</li><li>Bringing data into a pandas DataFrame</li><li>Inspecting a DataFrame object</li><li>Grabbing subsets of the data</li><li>Adding and removing data</li></ul><h4>Data Wrangling with Pandas</h4><ul><li>What is data wrangling?</li><li>Collecting temperature data</li><li>Cleaning up the data</li><li>Restructuring the data</li><li>Handling duplicate, missing, or invalid data</li></ul><h4>Aggregating Pandas DataFrames</h4><ul><li>Database-style operations on DataFrames</li><li>DataFrame operations</li><li>Aggregations with pandas and numpy</li><li>Time series</li></ul><h4>Visualizing Data with Pandas and Matplotlib</h4><ul><li>An introduction to matplotlib</li><li>Plotting with pandas</li><li>The pandas.plotting subpackage</li></ul><h4>Plotting with Seaborn and Customization Techniques</h4><ul><li>Utilizing seaborn for advanced plotting</li><li>Formatting</li><li>Customizing visualizations</li></ul><h4>Financial Analysis - Bitcoin and the Stock Market</h4><ul><li>Building a Python package</li><li>Data extraction with pandas</li><li>Exploratory data analysis</li><li>Technical analysis of financial instruments</li><li>Modeling performance</li></ul><h4>Rule-Based Anomaly Detection</h4><ul><li>Simulating login attempts</li><li>Exploratory data analysis</li><li>Rule-based anomaly detection</li></ul><h4>Getting Started with Machine Learning in Python</h4><ul><li>Learning the lingo</li><li>Exploratory data analysis</li><li>Preprocessing data</li><li>Clustering</li><li>Regression</li><li>Classification</li></ul><h4>Making Better Predictions - Optimizing Models</h4><ul><li>Hyperparameter tuning with grid search</li><li>Feature engineering</li><li>Ensemble methods</li><li>Inspecting classification prediction confidence</li><li>Addressing class imbalance</li><li>Regularization</li></ul><h4>Machine Learning Anomaly Detection</h4><ul><li>Exploring the data</li><li>Unsupervised methods</li><li>Supervised methods</li><li>Online learning</li></ul><h4>The Road Ahead</h4><ul><li>Data resources</li><li>Practicing working with data</li><li>Python practice</li></ul></div>

Nexus Human

Predictive Analytics

Big Data Analytics

Data Analysis

Data Analyst

Hands-on Data Analysis with Pandas (TTPS4878)

<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 the following participants:Cloud professionals interested in taking the Data Engineer certification exam.Data engineering professionals interested in taking the Data Engineer certification exam.</p>Overview<p>This course teaches participants the following skills:<br />Position the Professional Data Engineer Certification<br />Provide information, tips, and advice on taking the exam<br />Review the sample case studies<br />Review each section of the exam covering highest-level concepts sufficient to build confidence in what is known by the candidate and indicate skill gaps/areas of study if not known by the candidate<br />Connect candidates to appropriate target learning</p><p>This course will help prospective candidates plan their preparation for the Professional Data Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, quizzes, and discussions, candidates will familiarize themselves with the domain covered by the examination, to help them devise a preparation strategy. Rehearse useful skills including exam question reasoning and case comprehension. Tips and review of topics from the Data Engineering curriculum.</p></div><div id="course-pills-2"><h4>Understanding the Professional Data Engineer Certification</h4><ul><li>Position the Professional Data Engineer certification among the offerings</li><li>Distinguish between Associate and Professional</li><li>Provide guidance between Professional Data Engineer and Associate Cloud Engineer</li><li>Describe how the exam is administered and the exam rules</li><li>Provide general advice about taking the exam</li></ul><h4>Sample Case Studies for the Professional Data Engineer Exam</h4><ul><li>Flowlogistic</li><li>MJTelco</li><li></li></ul><h4>Designing and Building (Review and preparation tips)</h4><ul><li>Designing data processing systems</li><li>Designing flexible data representations</li><li>Designing data pipelines</li><li>Designing data processing infrastructure</li><li>Build and maintain data structures and databases</li><li>Building and maintaining flexible data representations</li><li>Building and maintaining pipelines</li><li>Building and maintaining processing infrastructure</li><li></li></ul><h4>Analyzing and Modeling (Review and preparation tips)</h4><ul><li>Analyze data and enable machine learning</li><li>Analyzing data</li><li>Machine learning</li><li>Machine learning model deployment</li><li>Model business processes for analysis and optimization</li><li>Mapping business requirements to data representations</li><li>Optimizing data representations, data infrastructure performance and cost</li><li></li></ul><h4>Reliability, Policy, and Security (Review and preparation tips)</h4><ul><li>Design for reliability</li><li>Performing quality control</li><li>Assessing, troubleshooting, and improving data representation and data processing infrastructure</li><li>Recovering data</li><li>Visualize data and advocate policy</li><li>Building (or selecting) data visualization and reporting tools</li><li>Advocating policies and publishing data and reports</li><li>Design for security and compliance</li><li>Designing secure data infrastructure and processes</li><li>Designing for legal compliance</li><li></li></ul><h4>Resources and next steps</h4><ul><li>Resources for learning more about designing data processing systems, data structures, and databases</li><li>Resources for learning more about data analysis, machine learning, business process analysis, and optimization</li><li>Resources for learning more about data visualization and policy Resources for learning more about reliability design</li><li>Resources for learning more about business process analysis and optimization</li><li>Resources for learning more about reliability, policies, security, and compliance     </li></ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans Preparing for the Professional Data Engineer Examination 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 Preparing for the Professional Data Engineer Examination 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>

Japanese Information Technology Engineers Examination

AWS Certified Developer

INCOSE Systems Engineering Professional Certification

AWS Certified Cloud Practitioner

AWS Certified Solutions Architect

Preparing for the Professional Data Engineer Examination

<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 Azure

Microsoft AZ-700

Azure Machine Learning

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

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

Python Machine Learning Bootcamp

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

Machine Learning with Real World Projects

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

Machine Learning (ML)

Machine Learning for Apps Level 4

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

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

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

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

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

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

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

Python Machine Learning Crash Course for Beginners

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

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

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

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

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

Machine Learning 101 with Scikit-learn and StatsModels

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

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

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

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

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

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

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

Machine Learning for Absolute Beginners - Level 2

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

TensorFlow

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

The Complete Machine Learning Course with Python

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

Learning To Learn

Machine Learning - Use in Flutter

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

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

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

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

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

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

Deep Learning

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

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