Name: Machine Learning Masterclass
Price: 19 GBP
Availability: InStock

<strong>Welcome to our hands-on video course, where you will learn technologies, such as React, Redux Toolkit, Express, and MongoDB. You will learn how to structure your code using Redux Toolkit, implement powerful features with React, and create a robust backend using Express and MongoDB. An understanding of modern JS fundamentals and the basics of React will be an add-on.</strong>
        <p>Welcome to our comprehensive course, which will cover all the essential topics and techniques you need to know to create a robust and scalable eCommerce application. We will begin by exploring the fundamentals of React, focusing on functional components and hooks. We will cover topics such as component state, props, and lifecycle methods, allowing you to build dynamic and interactive user interfaces.

Next, we will dive into React Router, a popular routing library for React applications. You will learn how to handle navigation, create dynamic routes, and pass parameters between different components. To enhance the visual appeal and responsiveness of your application, we will introduce React-Bootstrap, a versatile UI library. Managing the global state of your application is a critical aspect of building complex projects. We will introduce you to Redux, a popular state management library, and guide you through the process of integrating it into your eCommerce platform. You will learn about actions, reducers, and the Redux store, enabling you to manage the application's state efficiently.

You will learn how to handle routes, perform CRUD operations, and implement authentication and authorization mechanisms. You will integrate MongoDB, a popular NoSQL database, using the Mongoose Object Data Modeling (ODM) library. We will cover JWT authentication (JSON web tokens) with HTTP-Only cookies, providing a robust authentication mechanism to protect your users' sensitive data.

By the end of this course, you will have gained a comprehensive understanding of building a full-stack eCommerce platform from scratch.</p>
        <h2>What You Will Learn</h2><p>Explore the React-Bootstrap UI library<br>Learn to structure components<br>Manage global state with Redux (actions and reducers)<br>Create an extensive backend with Express<br>Learn JWT authentication HTTP-Only cookie<br>Create custom authentication middleware</p>
        <h2>Audience</h2><p>This course is designed for full-stack web developers who are interested in learning the MERN stack (MongoDB, Express, React, and Node.js) by building a real-world application. Whether you are a beginner looking to gain hands-on experience or an experienced developer seeking to expand your skill set, this course will provide you with the knowledge and practical skills needed to build a robust and scalable web application.</p>
        <h2>Approach</h2><p>Build a custom eCommerce platform with React, Redux, Node, Express, and MongoDB<br>Build an actual real-world project in a linear and progressive manner<br>Work with MongoDB database and the Mongoose ODM</p>
        <h2>Key Features</h2><p>Build a custom eCommerce platform with React, Redux, Node, Express, and MongoDB * Build an actual real-world project in a linear and progressive manner * Work with the MongoDB database and the Mongoose ODM</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/MERN-eCommerce-from-Scratch</p>
        <h2>About the Author</h2><h3>Brad Traversy</h3><p>Brad Traversy has been programming for around 12 years and teaching for almost 5 years. He is the owner of Traversy Media, which is a successful web development YouTube channel and specializes in everything from HTML5 to front-end frameworks such as Angular as well as server-side technologies such as Node.js, PHP, and Python. Brad has mastered explaining overly complex topics in a simple manner that is understandable. Invest in your knowledge by watching Brad's courses.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Starting The Front End</h4><p></p><br><h4>3. Serving & Fetching Data</h4><p></p><br><h4>4. Starting MongoDB & Mongoose</h4><p></p><br><h4>5.  Redux Toolkit Setup & State Management</h4><p></p><br><h4>6. Shopping Cart Functionality</h4><p></p><br><h4>7. Backend Authentication</h4><p></p><br><h4>8. Frontend Authentication</h4><p></p><br><h4>9. Checkout Process - Part 1</h4><p></p><br><h4>10.  Checkout Process - Part 2</h4><p></p><br><h4>11. Admin Functionality</h4><p></p><br><h4>12.  Reviews, Search & More</h4><p></p><br><h4>13. Deploy To Production</h4><p></p>

Packt

JavaScript

Web Development

MongoDB

Welcome to our hands-on video course, where you will learn technologies, such as React, Redux Toolkit, Express, and MongoDB. You will learn how to structure your code using Redux Toolkit, implement powerful features with React, and create a robust backend using Express and MongoDB. An understanding of modern JS fundamentals and the basics of React will be an add-on.

MERN eCommerce from Scratch

General Admission

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					<div><p><span>Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Machine Learning Course Using Python</span><span> has been specially designed to help learners gain a good command of Machine Learning Course Using Python</span><span>, providing them with a solid foundation of knowledge to become a qualified professional.</span></p><p><span>Through this Machine Learning Course Using Python</span><span>, you will gain both practical and theoretical understanding of Machine Learning Course Using Python</span><span> that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.</span></p><p><span>Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.</span></p></div>
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										<span>Get a deep understanding of the Machine Learning Course Using Python just in hours not years</span>
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										<span>Familiar yourself with the recent development and updates of the relevant industry</span>
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										<span>Get help from our expert tutors anytime you need</span>
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										<span> Access to course contents that are designed and prepared by industry professionals</span>
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										<span>Study at your convenient time and from wherever you want </span>
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										<span>No formal qualifications required, anyone from any academic background can take this course.</span>
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										<span>Access to any internet-enabled smart device.</span>
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				<h2>Why should I take this course?</h2>
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										<span>You will learn the researched and proven approach adopted by successful salespeople to transform their careers.</span>
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										<span>You will be able to incorporate various practical sales techniques successfully and understand your customers better.</span>
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                                <h2>Course Curriculum</h2>
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					<td colspan="4"><strong>Module 01: Introduction to Machine Learning Algorithms</strong></td>
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					<td>Introduction to types of ML algorithm</td><td><span>🕐 00:02:00</span></td>
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					<td colspan="4"><strong>Module 02: Preprocessing</strong></td>
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					<td>Importing a dataset in python</td><td><span>🕐 00:02:00</span></td>
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					<td>Resolving Missing Values</td><td><span>🕐 00:06:00</span></td>
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					<td>Managing Category Variables</td><td><span>🕐 00:04:00</span></td>
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					<td>Normalizing Variables - Python Code</td><td><span>🕐 00:03:00</span></td>
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					<td>Simple Linear Regression - How it works?</td><td><span>🕐 00:04:00</span></td>
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					<td>kNN - How it works?</td><td><span>🕐 00:02:00</span></td>
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					<td>kNN - Python Implementation</td><td><span>🕐 00:10:00</span></td>
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					<td>Decision Tree Classifier and Random Forest Classifier in Python</td><td><span>🕐 00:10:00</span></td>
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					<td>SVM - How it works?</td><td><span>🕐 00:04:00</span></td>
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					<td>SVM - Python Implementation</td><td><span>🕐 00:06:00</span></td>
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					<td colspan="4"><strong>Resources</strong></td>
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					<td>Resources - Machine Learning with Python</td><td><span>🕐 00:00:00</span></td>
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Skill Up

Learning & Development

Online Course Marketing

Machine Learning (ML)

Learning To Learn

Trainers Course

Azure Machine Learning

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step

Machine Learning Course Using Python

<strong>This course will help you to gain a mastery level understanding of the fundamentals of Android Studio, Android app development, and the Kotlin programming language by building six full-fledged applications as well as many more 'learning' applications throughout the course.</strong>
        <p>The Art of Doing: Dive into Android development with Kotlin is a course that takes the time to lay a foundation and build upon it. We won't just get Android Studio installed and rush through all it does for us in creating a project. Instead, we will walk through each file created and the given starter code, so you feel like you are in control of the applications you are writing! We'll continue on this trend of fully explaining and gaining a mastery level of understanding of concepts as we explore various views, layout styles, view and data binding, fragments and navigation, and lastly MVVM architecture.

In this course, we will walk through, step by step, how to design the layout and the functionality of unique, engaging, and purposeful apps. Together, we will work through 15 sections of this course. Each section will highlight concepts and ideas, explaining every step along the way and answering any questions you might have.

By the end of this course, you will be coming up with your own app ideas and feel confident enough in your abilities to create them.

The complete code bundle for this course is available at https://github.com/PacktPublishing/The-Art-of-Doing-Dive-Into-Android-Development-with-Kotlin</p>
        <h2>What You Will Learn</h2><p>Create your own Android apps using Android Studio and Kotlin<br>Test and upload your applications on your own physical/emulated devices<br>How to design app layouts using linear and constraint layouts<br>How to use binding objects (view and data binding) to improve projects<br>How to implement navigation and fragments for dynamic apps<br>How to implement view models and live data</p>
        <h2>Audience</h2><p>This course is designed for beginner Android students looking to understand Android application development with Kotlin.<br><br>Also, students who have gained an introductory level knowledge in another language (Python, JavaScript) and are looking for ways to apply their knowledge can benefit from this course.<br><br>One must have a basic understanding of computer programming principles such as variables, conditionals, loops, and functions as these won't be covered. Also, have a computer capable of running Android Studio or the Android Emulator or a physical Android device for testing.</p>
        <h2>Approach</h2><p>This is a practical course where we will be building a few apps to master the fundamentals of android application development. The goal for this course is not to expose you to every single concept or idea in Android development but rather provide you with the structure and context to make you feel like you fully understand the fundamentals presented to you in this course.</p>
        <h2>Key Features</h2><p>A complete practical course to master Android application development * Create 6 full-fledge applications as well as many more "learning" applications throughout the course * Learn to code in Kotlin, Google's official language for Android application development</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/The-Art-of-Doing-Dive-Into-Android-Development-with-Kotlin</p>
        <h2>About the Author</h2><h3>Michael Eramo</h3><p>Michael Eramo is a life-long learner, a self-taught programmer, and an experienced educator. He holds official bachelor's degrees in music, education, and physics and a master's in mathematics. He is also a Microsoft certified software developer.

He has years of experience as a high school physics teacher, computer science teacher, and college mathematics teacher. He is a part of the New York State Master-Teacher Program, a network of more than 800 outstanding public-school teachers throughout the state who share a passion for STEM learning and for collaborating with colleagues to inspire the next generation of STEM leaders.</p>
        <h2>Course Outline</h2><h4>1. Installation and Setup</h4><p></p><br><h4>2. Exploring the Basics of Android Studio</h4><p></p><br><h4>3. The Linear Layout:  A First Look</h4><p></p><br><h4>4. Motivate Me App</h4><p></p><br><h4>5. Counter App</h4><p></p><br><h4>6. The Constraint Layout:  A First Look</h4><p></p><br><h4>7. Coin Flip App</h4><p></p><br><h4>8. Diaper Tracker App</h4><p></p><br><h4>9. Using onSaveInstanceState</h4><p></p><br><h4>10. ViewBinding</h4><p></p><br><h4>11. DataBinding</h4><p></p><br><h4>12. Fragments and Navigation:  A First Look</h4><p></p><br><h4>13. Score Keeper App</h4><p></p><br><h4>14. View Models and Live Data:  A First Look</h4><p></p><br><h4>15. Click! The App</h4><p></p><br><h4>16. Thank You</h4><p></p>

Android Development

Mobile

Kotlin

Android

This course will help you to gain a mastery level understanding of the fundamentals of Android Studio, Android app development, and the Kotlin programming language by building six full-fledged applications as well as many more 'learning' applications throughout the course.

The Art of Doing: Dive Into Android Development with Kotlin

<strong>In this course, we'll learn how to visualize data with real weather data downloaded from the US National Weather Service using the R programming language and RStudio.

We recommend that you have some background in HTML, CSS, and JavaScript. You don't need to be an expert by any means, but you should have experience building web pages with HTML and CSS, and you should have basic programming skills with JavaScript.</strong>
        <p>Welcome to How to Visualize Data with R. In this course, You'll learn how to use the R programming language and RStudio to visualize data. Elisabeth will take you through building a visualization using data downloaded from the US National Weather Service. You'll learn how to use R to read data from a CSV file, inspect and understand data and data frames, and use the plot() and ggplot() functions to create data visualizations. Along the way, you'll learn from the ground up how to use R and RStudio, including how to create and run an R script, basic R data types and values, how to create a scatter plot graph, how linear regression works, and how to install and use an R package.

At the end of the course, you'll have completed a data visualization of the weather data and have some new skills you can apply to your own data too.

The code bundle for this course is available at https://github.com/PacktPublishing/How-to-Visualize-Data-with-R</p>
        <h2>What You Will Learn</h2><p>Build data visualization using R programming language and RStudio<br>Use R to read data from a CSV file, inspect and understand data frames<br>Use the plot() and ggplot() functions to create data visualizations<br>Learn how to use R and RStudio<br>Create and run an R script and learn basic R data types and values<br>Create a scatter plot graph and learn how linear regression works</p>
        <h2>Audience</h2><p>To successfully complete this project, we recommend that you have some background in HTML and JavaScript. You don't need to be an expert by any means, but you should have experience building web pages and you should have basic programming skills with JavaScript. No experience with RStudio is required.</p>
        <h2>Approach</h2><p>This is a complete hands-on course where we will work on visualizing data with real weather data using the R programming language and RStudio.</p>
        <h2>Key Features</h2><p>Visualize data with R using real weather data downloaded from the US National Weather Service * Create a trend line with a linear model function * Create scatter plot graphs from temperature data</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/How-to-Visualize-Data-with-R</p>
        <h2>About the Author</h2><h3>Elisabeth Robson</h3><p>Elisabeth Robson is a co-founder and principal at WickedlySmart, an author and software developer, and teaches online courses for O'Reilly Media and LinkedIn Learning. She's the co-author of four best-selling titles-Head First JavaScript Programming, Head First Design Patterns, Head First HTML & CSS, and Head First HTML5 Programming-and was previously the director of special projects and co-director of the Head First series at O'Reilly Media. Prior to her work at O'Reilly Media, Elisabeth was the director of engineering at The Walt Disney Company. Elisabeth holds a master's degree in computer science from Yale University.</p>
        <h2>Course Outline</h2><h4>1. Get Started</h4><p></p><br><h4>2. Work with the Data</h4><p></p><br><h4>3. D3 Patterns and Idioms</h4><p></p><br><h4>4. Create the Visualization</h4><p></p><br><h4>5. Wrap up</h4><p></p>

Data Visualization

HTML

jQuery

In this course, we'll learn how to visualize data with real weather data downloaded from the US National Weather Service using the R programming language and RStudio.

We recommend that you have some background in HTML, CSS, and JavaScript. You don't need to be an expert by any means, but you should have experience building web pages with HTML and CSS, and you should have basic programming skills with JavaScript.

How to Visualize Data with R

<p>You do not need any lectures regarding the importance of Maths and English, do you? To suffice, one is the language of science, and the other is the international language. Unfortunately, we often struggle in both, thanks to the lack of a solid base. Our Learning Maths &amp; English GCSE bundle can give you a firm foundation or help you to pass your GCSE exam with flying colours!</p><p>This Learning Maths &amp; English GCSE bundle consists of 12 individual courses. This bundle can be primarily divided into two parts. The first part, consisting of six courses, covers fundamentals and advanced mathematics. You will learn the basic concepts of Algebra, high school maths, as well as advanced maths and calculus. The other six courses deal with English language essentials. You will gain an understanding of GCSE English, TESOL, spelling, punctuation, and basics of grammar etc. Moreover, you will learn 200 common English idioms and phrases along with essay writing in English.</p><p>Be the master of numbers and letters, then? Enrol immediately in this Learning Maths &amp; English GCSE and firm your base. Happy learning!</p><p>Along with this Learning Maths &amp; English GCSE Course - CPD Accredited course, you will get <strong>11 premium courses, an original</strong><strong>hardcopy</strong>, <strong> 12 PDF certificates (Main Course + Additional Courses) </strong>Student ID card as gifts.</p><p><strong>This Learning Maths &amp; English GCSE Course - CPD Accredited Bundle Consists of the following Premium courses: </strong></p><ul><li><strong></strong><strong> Course 01: </strong>Functional Skills Maths Level 2</li><li><strong></strong><strong> Course 02: </strong>Mathematics Fundamentals</li><li><strong></strong><strong> Course 03: </strong>Algebra Fundamentals</li><li><strong></strong><strong> Course 04: </strong>Calculus Level 1 - Learn Differentiation</li><li><strong></strong><strong> Course 05: </strong>Advanced Mathematics</li><li><strong></strong><strong> Course 06: </strong>High School Math</li><li><strong></strong><strong> Course 07: </strong>GCSE English</li><li><strong></strong><strong> Course 08: </strong>Diploma in TEFL/TESOL</li><li><strong></strong><strong> Course 09: </strong>Functional Skills: English</li><li><strong></strong><strong> Course 10: </strong>English Spelling, Punctuation, and Grammar Basic Course</li><li><strong></strong><strong> Course 11: </strong>200 Common English Idioms and Phrases</li><li><strong></strong><strong> Course 12: </strong>Essay Writing in English</li></ul><p><strong>Learning Outcome</strong></p><p>Upon completion of this Learning Maths &amp; English GCSE bundle, you will be able to -</p><ul><li>Learn about major mathematical concepts such as Decimal numbers, Rational Numbers, Approximation etc</li><li>Understand the BODMAS Simplification Rule</li><li>Consolidate your understanding of fundamental mathematics</li><li>Learn differential calculus</li><li>Understand advanced mathematical ideas such as Matrices, Trigonometric Functions and Trigonometric Functions</li><li>Comprehend the rudiments of English</li><li>Grasp sentence structure, verb usage, capitalisation and punctuation rules in detail</li><li>Learn 200 common English idioms and phrases</li></ul><p>Enrol now in Learning Maths &amp; English GCSE Course - CPD Accredited to advance your career, and use the premium study materials from Apex Learning.</p><p><strong>Curriculum of the Bundle </strong></p><p><strong>Course 01: Functional Skills Maths Level 2</strong></p><ul><li>Module 1: Numbers and Negative Numbers</li><li>Module 2: Multiples Factors</li><li>Module 3: Fractions and Power</li><li>Module 4: Percentages</li><li>Module 5: Expressions</li><li>Module 6: Decimals</li><li>Module 7: Ratio and Proportion</li><li>Module 8: Exponents and Radicals</li><li>Module 9: Graphs</li><li>Module 10: The Profit and Loss</li><li>Module 11: Perimeter and Area</li><li>Module 12: Averages</li><li>Module 13: Probability</li></ul><p><strong>Course 02: Mathematics Fundamentals<br /></strong></p><ul><li>Integers (Directed Numbers)</li><li>Factors and Multiples</li><li>Fractions</li><li>Simplification Rule : BODMAS</li><li>Decimal numbers</li><li>Rational Numbers</li><li>Approximation</li><li>Estimation and Scientific Notation or Standard form</li><li>Percentage</li><li>Ratio and Proportion</li><li>Unitary Method and its Applications</li><li>Profit , Loss, discount and Tax</li></ul><p><strong>Course 03: Algebra Fundamentals<br /></strong></p><ul><li>Introduction</li><li>Fundamental concepts on Algebraic Expressions</li><li>Operations on Algebraic Expressions</li><li>Indices ( Exponents)</li><li>Multiplication and Division of Algebraic expressions</li><li>Brackets in Algebra</li><li>Linear equations in one variable</li><li>Algebraic Identities</li><li>Formula : Change of subject of formula</li><li>Linear Inequalities</li><li>Resolve into factors</li><li>Algebraic Fractions</li><li>Coordinate axis - points and Line graph</li><li>System of simultaneous linear equations in two variables</li><li>Polynomials</li><li>Quadratic Polynomials</li><li>Quadratic Equations</li></ul><p><strong>Course 04: Calculus Level 1 - Learn Differentiation<br /></strong></p><ul><li>Section 01: Introduction</li><li>Section 02: Fundamental Rules for Differentiation</li><li>Section 03: Differentiation of Trigonometric Functions</li><li>Section 04: Differentiation of Exponential Functions</li><li>Section 05: Differentiation of Logarithmic Functions</li><li>Section 06: Revision Section 0on Chain Rule</li><li>Section 07: Differentiation of inverse Trig Function</li><li>Section 08: Differentiation of Hyperbolic Trig Functions</li><li>Section 09: Differentiation of Implicit functions</li><li>Section 10: Logarithmic Differentiation</li><li>Section 11: Differentiation of Parametric Functions</li><li>Section 12: Differentiation of Higher order derivatives</li></ul><p><strong>Course 05: Advanced Mathematics<br /></strong></p><ul><li>Unit 01: Introduction</li><li>Unit 02: Mathematical Logic</li><li>Unit 03: Matrices</li><li>Unit 04: Trigonometric Functions</li><li>Unit 05: Pair of Straight Line</li><li>Unit 06: Lines &amp; Planes</li><li>Unit 07: Linear Programming</li></ul><p><strong>Course 06: High School Math<br /></strong></p><ul><li>Introduction</li><li>Functions</li><li>Quadratic Equations</li><li>Co-ordinate Geometry</li><li>Sequence and series</li><li>Binomial Theorem</li><li>Differentiation</li><li>Tangents and Normals</li><li>Stationary Points &amp; Curve Sketching</li><li>Second Derivative Test (Maximum &amp; Minimum Points)</li><li>Simultaneous Linear equations</li><li>Essential Revision</li></ul><p><strong>Course 07: GCSE English<br /></strong></p><ul><li>Module 01: Introduction to GCSE English</li><li>Module 02: Edexcel English Section A Part 01</li><li>Module 03: Edexcel English Section A Part 02</li><li>Module 04: Edexcel English Section B</li><li>Module 05: Edexcel English Section C</li><li>Module 06: Cambridge English Exercise 1-4</li><li>Module 07: Cambridge English Exercise 5</li><li>Module 08: Cambridge English Exercise 6</li></ul><p><strong>Course 08: Diploma in TEFL/TESOL<br /></strong></p><ul><li>Module 01: Introduction to English Language Teaching &amp; Learning</li><li>Module 02: Linguistics</li><li>Module 03: English Pronunciation</li><li>Module 04: Teaching Grammar and Vocabulary</li><li>Module 05: Language Teaching Methodologies</li><li>Module 06: Teaching Receptive Skills: Reading</li><li>Module 07: Teaching Productive Skills: Writing</li><li>Module 08: Teaching Receptive Skills: Listening</li><li>Module 09: Teaching Productive Skills: Speaking</li><li>Module 10: Lesson Planning and Creating Materials</li><li>Module 11: Use of Games and Activities</li><li>Module 12: Technology in Second Language Teaching &amp; Learning</li><li>Module 13: Classroom Management and Student Motivation</li><li>Module 14: Teaching English: Situational Approach</li><li>Module 15: Professional Development and Employment</li></ul><p><strong>Course 09: Functional Skills: English</strong></p><ul><li>Module 1: Introduction to the Course</li><li>Module 2: Basics of Grammar</li><li>Module 3: The Basics of Sentence</li><li>Module 4: Structure of Sentence</li><li>Module 5: Question</li><li>Module 6: Punctuation &amp; Capitalisation</li><li>Module 7: Spelling</li><li>Module 8: Common Mistakes &amp; Ways to Improve</li></ul><p><strong>Course 10: English Spelling, Punctuation, and Grammar Basic Course</strong></p><ul><li>Module 1: Introduction to the Course</li><li>Module 2: The Basics of Grammar</li><li>Module 3: The Basics of Sentence</li><li>Module 4: Structure of Sentence</li><li>Module 5: Question</li><li>Module 6: Punctuation and Capitalisation</li><li>Module 7: Spelling</li><li>Module 8: Common Mistakes &amp; Ways to Improve<br /><br /></li></ul><p><strong>Course 11: 200 Common English Idioms and Phrases<br /></strong></p><ul><li>Section 01: Introduction</li><li>Section 02: Idioms and Phrases Unit 1</li><li>Section 03: Idioms and Phrases Unit 2</li><li>Section 04: Idioms and Phrases Unit 3</li><li>Section 05: Idioms and Phrases Unit 4</li><li>Section 06: Idioms and Phrases Unit 5</li><li>Section 07: Idioms and Phrases Unit 6</li><li>Section 08: Idioms and Phrases Unit 7</li><li>Section 09: Idioms and Phrases Unit 8</li><li>Section 10: Idioms and Phrases Unit 9</li><li>Section 11: Idioms and Phrases Unit 10</li></ul><p><strong>Course 12: 200 Common English Idioms and Phrases</strong></p><ul><li>Module 01: Introduction</li><li>Module 02: The Narrative Essay</li><li>Module 03: The Narrative Essay / part 2</li><li>Module 04: Introduction to the Essay 'Shooting an Elephant'</li><li>Module 05: A reading of 'Shooting an Elephant'</li><li>Module 06: Discussion on 'Shooting an Elephant'</li><li>Module 07: Introduction to 2nd Essay - 'Most Important Day'</li><li>Module 08: A reading - 'Most Important Day'</li><li>Module 09: Discussion - 'Most Important Day'</li><li>Module 10: Prompt for your own Narrative Essay<strong><br /></strong></li></ul><h2>
        
        <div>CPD</div>
    </h2>

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                                        120 CPD hours / points
                                    </div>
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                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>Anyone from any background can enrol in this Learning Maths &amp; English GCSE Course - CPD Accredited bundle.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>This Learning Maths &amp; English GCSE Course - CPD Accredited course has been designed to be fully compatible with tablets and smartphones.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Building a solid foundation in maths and English will benefit you in every career prospect. Specifically, This Learning Maths &amp; English GCSE course will lead you to some particular career opportunities, such as;</p><ul><li>Teacher - £30,000 - £41,000 Annually</li><li>Private Tutor - £20,00 - £25.00 Annually</li><li>Finance Manager - £50,000 - £60,000 Annually</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

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                                    <h4>Certificate of completion</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>
            <div> <p>You will get the Hard Copy certificate for the title course (<strong>Functional Skills Maths Level 2</strong>) absolutely Free! Other Hard Copy certificates are available for £10 each. </p><p>Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.</p> </div>

                        </div>

Apex Learning

Learning Maths & English GCSE Preparation Course - CPD Accredited

<strong>This comprehensive deep learning course with Python will start with the basics and work up to advanced topics such as using different frameworks in Python to solve real-world problems and building artificial neural networks with TensorFlow and Keras. </strong>
        <p>Python is famed as one of the best programming languages for its flexibility. It works in almost all fields, from web development to developing financial applications. However, it's no secret that Python's best application is in deep learning and artificial intelligence (AI)  tasks.

You'll start with an introduction to deep learning where you'll focus on the fundamentals of the deep learning theory and find out how to use deep learning in Python, before moving on to artificial neural networks (ANNs). You'll learn how to use different frameworks in Python to solve real-world problems using deep learning and artificial intelligence. Next, you'll discover how to make predictions using linear regression, polynomial regression, and multivariate regression, and build artificial neural networks with TensorFlow and Keras. The video also covers convolutional neural networks (CNNs) at length and goes through the different components such as convolution layer, pooling layer, and fully connected layer. Finally, you'll wrap up CNN  implementation  in Python.

By the end of this course, you'll be able to use the concepts of deep learning to build neural networks in Python like a professional.</p>
        <h2>What You Will Learn</h2><p>Get to grips with the fundamentals of the deep learning theory<br>Discover how to use deep learning in Python<br>Find out how to use different frameworks in Python<br>Build artificial neural networks with TensorFlow and Keras<br>Explore the implementation of ANNs and CNNs in Python</p>
        <h2>Audience</h2><p>This course is for both programming beginners and professionals who want to expand on their knowledge of deep learning, or professional mathematicians looking to learn how to analyze data programmatically. Prerequisites include basic mathematical skills and Python coding experience.</p>
        <h2>Approach</h2><p>This fundamentals training is excellent for both newcomers and seasoned professionals. This is the best course for you if you're looking for one that starts with the fundamentals and progresses to more complex subjects. It does not teach anything but what is necessary to get started with deep learning. This is pretty much everything you need to get started with the topic, and it also helps to keep the course quite concise.</p>
        <h2>Key Features</h2><p>Fundamentals course designed for both beginners and experts alike * Use different frameworks in Python to solve real-world problems using deep learning and AI * Make predictions using linear regression, polynomial regression, and multivariate regression</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-for-Deep-Learning---Build-Neural-Networks-in-Python</p>
        <h2>About the Author</h2><h3>Meta Brains</h3><p>Meta Brains is a professional training brand developed by a team of software developers and finance professionals who have a passion for finance, coding, and Excel. They bring together both professional and educational experiences to create world-class training programs accessible to everyone.

Currently, they're focused on the next great revolution in computing: The Metaverse. Their ultimate objective is to train the next generation of talent so that we can code and build the metaverse together!</p>
        <h2>Course Outline</h2><h4>1. Introduction to Deep Learning</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Course Introduction</p><p style='margin: 0'>In this video, we will have a quick introduction to the course.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Deep Learning?</p><p style='margin: 0'>In this video, we will understand what deep learning is.</p></td></tr><tr><td style='padding: 15px;'><p>3. Why is Deep Learning Important?</p><p style='margin: 0'>In this video, we will understand the importance of deep learning.</p></td></tr><tr><td style='padding: 15px;'><p>4. Software and Frameworks</p><p style='margin: 0'>In this video, you will learn about software and frameworks.</p></td></tr></tbody></table><br><h4>2. Artificial Neural Networks (ANN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Section Introduction</p><p style='margin: 0'>In this video, we will have a quick introduction to this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Anatomy and Function of Neurons</p><p style='margin: 0'>In this video, you will learn about the anatomy and function of neurons.</p></td></tr><tr><td style='padding: 15px;'><p>3. An Introduction to The Neural Network</p><p style='margin: 0'>In this video, we will have a quick introduction to the neural network.</p></td></tr><tr><td style='padding: 15px;'><p>4. Architecture of a Neural Network</p><p style='margin: 0'>In this video, you will learn about the architecture of a neural network.</p></td></tr></tbody></table><br><h4>3. Propagation of Information in ANNs</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Feed-Forward and Back Propagation Networks</p><p style='margin: 0'>In this video, you will learn about feed-forward and back propagation networks.</p></td></tr><tr><td style='padding: 15px;'><p>2. Backpropagation in Neural Networks</p><p style='margin: 0'>In this video, you will learn about backpropagation in neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. Minimizing the Cost Function Using Backpropagation</p><p style='margin: 0'>In this video, you will learn how to minimize the cost function using backpropagation.</p></td></tr></tbody></table><br><h4>4. Neural Network Architectures</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Single Layer Perceptron (SLP) Model</p><p style='margin: 0'>In this video, you will learn about the Single-layer perceptron (SLP) model.</p></td></tr><tr><td style='padding: 15px;'><p>2. Radial Basis Network (RBN)</p><p style='margin: 0'>In this video, you will learn about Radial Basis Network (RBN).</p></td></tr><tr><td style='padding: 15px;'><p>3. Multi-Layer Perceptron (MLP) Neural Network</p><p style='margin: 0'>In this video, you will learn about multi-layer perceptron (MLP) Neural Network.</p></td></tr><tr><td style='padding: 15px;'><p>4. Recurrent Neural Network (RNN)</p><p style='margin: 0'>In this video, you will learn about Recurrent Neural Network (RNN).</p></td></tr><tr><td style='padding: 15px;'><p>5. Long Short-Term Memory (LSTM) Networks</p><p style='margin: 0'>In this video, you will learn about Long Short-Term Memory (LSTM) networks.</p></td></tr><tr><td style='padding: 15px;'><p>6. Hopfield Neural Network</p><p style='margin: 0'>In this video, you will learn about Hopfield neural network.</p></td></tr><tr><td style='padding: 15px;'><p>7. Boltzmann Machine Neural Network</p><p style='margin: 0'>In this video, you will learn about Boltzmann Machine Neural Network.</p></td></tr></tbody></table><br><h4>5. Activation Functions</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is the Activation Function?</p><p style='margin: 0'>In this video, we will understand what the Activation function is.</p></td></tr><tr><td style='padding: 15px;'><p>2. Important Terminologies</p><p style='margin: 0'>In this video, we will understand some important terminologies like differential function and monotonic function.</p></td></tr><tr><td style='padding: 15px;'><p>3. The Sigmoid Function</p><p style='margin: 0'>In this video, you will learn about the Sigmoid function.</p></td></tr><tr><td style='padding: 15px;'><p>4. Hyperbolic Tangent Function</p><p style='margin: 0'>In this video, you will learn about the Hyperbolic tangent function.</p></td></tr><tr><td style='padding: 15px;'><p>5. SoftMax Function</p><p style='margin: 0'>In this video, you will learn about the SoftMax function.</p></td></tr><tr><td style='padding: 15px;'><p>6. Rectified Linear Unit (ReLU) Function</p><p style='margin: 0'>In this video, you will learn about Rectified Linear Unit (ReLU) function.</p></td></tr><tr><td style='padding: 15px;'><p>7. Leaky Rectified Linear Unit function</p><p style='margin: 0'>In this video, you will learn about the Leaky Rectified Linear Unit function.</p></td></tr></tbody></table><br><h4>6. Gradient Descent Algorithm</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is Gradient Descent?</p><p style='margin: 0'>In this video, we will understand what Gradient Descent is.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Stochastic Gradient Descent?</p><p style='margin: 0'>In this video, we will understand what is Stochastic Gradient Descent?</p></td></tr><tr><td style='padding: 15px;'><p>3. Gradient Descent versus Stochastic Gradient Descent</p><p style='margin: 0'>In this video, you will learn about Gradient Decent versus Stochastic Gradient Descent.</p></td></tr></tbody></table><br><h4>7. Summary - Overview of Neural Networks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. How do Artificial Neural Networks Work?</p><p style='margin: 0'>In this video, we will understand how artificial neural networks work.</p></td></tr><tr><td style='padding: 15px;'><p>2. Advantages of Neural Networks</p><p style='margin: 0'>In this video, you will learn about the advantages of neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. Disadvantages of Neural Networks</p><p style='margin: 0'>In this video, you will learn about the disadvantages of neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>4. Applications of Neural Networks</p><p style='margin: 0'>In this video, you will learn about the applications of neural networks.</p></td></tr></tbody></table><br><h4>8. Implementation of ANN in Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will have a quick Introduction to this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Exploring the Dataset</p><p style='margin: 0'>In this video, you will learn how to explore the dataset.</p></td></tr><tr><td style='padding: 15px;'><p>3. Problem Statement</p><p style='margin: 0'>In this video, we will understand the problem statement.</p></td></tr><tr><td style='padding: 15px;'><p>4. Data Pre-Processing</p><p style='margin: 0'>In this video, you will learn how data pre-processing works.</p></td></tr><tr><td style='padding: 15px;'><p>5. Loading the Dataset</p><p style='margin: 0'>In this video, you will learn how to load the dataset.</p></td></tr><tr><td style='padding: 15px;'><p>6. Splitting the Dataset into Independent and Dependent Variables</p><p style='margin: 0'>In this video, you will learn how to split the dataset into independent and dependent variables.</p></td></tr><tr><td style='padding: 15px;'><p>7. Label Encoding Using Scikit-Learn</p><p style='margin: 0'>In this video, you will learn how to work with label encoding using scikit-learn</p></td></tr><tr><td style='padding: 15px;'><p>8. One-hot encoding using scikit-learn</p><p style='margin: 0'>In this video, we will learn how to work with One-hot encoding using scikit-learn.</p></td></tr><tr><td style='padding: 15px;'><p>9. Training and Test Sets: Splitting Data</p><p style='margin: 0'>In this video, we will be working on splitting the data with training and test sets.</p></td></tr><tr><td style='padding: 15px;'><p>10. Feature Scaling</p><p style='margin: 0'>In this video, you will learn about feature scaling.</p></td></tr><tr><td style='padding: 15px;'><p>11. Building the Artificial Neural Network</p><p style='margin: 0'>In this video, you will learn how to build the Artificial Neural Network</p></td></tr><tr><td style='padding: 15px;'><p>12. Adding the Input Layer and the First Hidden Layer</p><p style='margin: 0'>In this video, you will learn how to add the input layer and the first hidden layer.</p></td></tr><tr><td style='padding: 15px;'><p>13. Adding the Next Hidden Layer</p><p style='margin: 0'>In this video, you will learn how to add the next hidden layer.</p></td></tr><tr><td style='padding: 15px;'><p>14. Adding the Output Layer</p><p style='margin: 0'>In this video, you will learn how to add the output layer.</p></td></tr><tr><td style='padding: 15px;'><p>15. Compiling the Artificial Neural Network</p><p style='margin: 0'>In this video, you will learn how to compile the Artificial Neural Network.</p></td></tr><tr><td style='padding: 15px;'><p>16. Fitting the ANN Model to the Training Set</p><p style='margin: 0'>In this video, you will learn how to fit the ANN model into the training set.</p></td></tr><tr><td style='padding: 15px;'><p>17. Predicting the Test Set Results</p><p style='margin: 0'>In this video, you will learn how to predict the test set results.</p></td></tr></tbody></table><br><h4>9. Convolutional Neural Networks (CNN)</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>In this video, we will have a quick Introduction to the learning goals for this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Components of Convolutional Neural Networks</p><p style='margin: 0'>In this video, you will learn about the components of Convolutional Neural Networks.</p></td></tr><tr><td style='padding: 15px;'><p>3. Convolution Layer</p><p style='margin: 0'>In this video, you will learn about the Convolution layer.</p></td></tr><tr><td style='padding: 15px;'><p>4. Pooling Layer</p><p style='margin: 0'>In this video, you will learn about Pooling layer.</p></td></tr><tr><td style='padding: 15px;'><p>5. Fully Connected Layer</p><p style='margin: 0'>In this video, you will learn about fully connected layer.</p></td></tr></tbody></table><br><h4>10. Implementation of CNN in Python</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Dataset</p><p style='margin: 0'>In this video, we are going to use the MNIST handwritten digit data which contains the images of handwritten digits.</p></td></tr><tr><td style='padding: 15px;'><p>2. Importing Libraries</p><p style='margin: 0'>In this video, you will learn how to import libraries.</p></td></tr><tr><td style='padding: 15px;'><p>3. Building the CNN Model</p><p style='margin: 0'>In this video, you will learn how to build the Convolutional Neural Networks (CNN) model.</p></td></tr><tr><td style='padding: 15px;'><p>4. Accuracy of the Model</p><p style='margin: 0'>In this video, you will learn about the accuracy of the Convolutional Neural Networks (CNN) model.</p></td></tr></tbody></table>

Data Science

Python

TensorFlow

This comprehensive deep learning course with Python will start with the basics and work up to advanced topics such as using different frameworks in Python to solve real-world problems and building artificial neural networks with TensorFlow and Keras.

Python for Deep Learning - Build Neural Networks in Python

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					<p><span>This comprehensive course on <b>Machine Learning with Python</b> will deepen your understanding on this topic.</span></p><p><span>After successful completion of this course you can acquire the required skills in this sector. This <b>Machine Learning with Python</b> comes with accredited certification, which will enhance your CV and make you worthy in the job market.</span></p><p><span>So enrol in this course today to fast track your career ladder.</span></p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.</p>					</div>
						</div>
				</div>
				<div>
				<div>
			<h2>Who is This course for?</h2>		</div>
				</div>
				<div>
				<div>
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					<p><span>There is no experience or previous qualifications required for enrolment on this <b>Machine Learning with Python</b>. It is available to all students, of all academic backgrounds.</span></p>					</div>
						</div>
				</div>
				<div>
				<div>
			<h2>Requirements</h2>		</div>
				</div>
				<div>
				<div>
								<div>
					<p><span>Our <b>Machine Learning with Python</b> is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</span></p><p><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></p>					</div>
						</div>
				</div>
				<div>
				<div>
			<h2>Career Path</h2>		</div>
				</div>
				<div>
				<div>
								<div>
					<p><span>Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business &amp; Management, Admin, Accountancy &amp; Finance, Secretarial &amp; PA, Teaching &amp; Mentoring etc.</span></p>					</div>
						</div>
				</div>
						</div>
					</div>
		</div>
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					</div>
		</section>
						</div>
						</div>
					</div> <h3>Course Curriculum</h3><div><p>4 sections • 21 lectures • 01:34:00 total length</p></div><div><div><div><div><div><div><div><p>•Introduction to types of ML algorithm: 00:02:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•SVM - Python Implementation: 00:06:00</p></div><div><div><p>•Introduction to types of ML algorithm: 00:02:00</p></div><div><div><p>•Importing a dataset in python: 00:02:00</p></div><div><div><p>•Resolving Missing Values: 00:06:00</p></div><div><div><p>•Managing Category Variables: 00:04:00</p></div><div><div><p>•Training and Testing Datasets: 00:07:00</p></div><div><div><p>•Normalizing Variables: 00:02:00</p></div><div><div><p>•Normalizing Variables - Python Code: 00:03:00</p></div><div><div><p>•Summary: 00:01:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Simple Linear Regression - How it works?: 00:04:00</p></div><div><div><p>•Simple Linear Regreesion - Python Implementation: 00:07:00</p></div><div><div><p>•Multiple Linear Regression - How it works?: 00:01:00</p></div><div><div><p>•Multiple Linear Regression - Python Implementation: 00:09:00</p></div><div><div><p>•Decision Trees - How it works?: 00:05:00</p></div><div><div><p>•Random Forest - How it works?: 00:03:00</p></div><div><div><p>•Decision Trees and Random Forest - Python Implementation: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•kNN - How it works?: 00:02:00</p></div><div><div><p>•kNN - Python Implementation: 00:10:00</p></div><div><div><p>•Decision Tree Classifier and Random Forest Classifier in Python: 00:10:00</p></div><div><div><p>•SVM - How it works?: 00:04:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Managed Learning Programme (MLP)

Machine Learning with Python

<p>In today's digital age, mastery in visual arts and multimedia plays a pivotal role in various professions. The UK sees a soaring demand for professionals proficient in graphic design, multimedia editing, and digital artistry. With potential high-income prospects and growing job demand, isn't it time you equipped yourself?</p><p>Our comprehensive Adobe Course Bundle covering Photoshop, Illustrator, Premiere Pro CC, InDesign, Lightroom CC, and After Effects, promises to lay a solid foundation for your future creative endeavours.</p><p>Whether you're looking to design captivating graphics, stunning websites, or high-quality promotional videos, our meticulously designed bundle covers it all. Equip yourself with the critical tools and techniques used in Photoshop and Illustrator, master the art of non-linear video editing with Premiere Pro CC, and learn how to craft attractive layouts with InDesign.</p><p>Don't miss this opportunity! Enrol in our Adobe all within one all-inclusive bundle!</p><p><strong>This Animal Nutrition &amp; Diet Course (Dog and Cat Nutrition) Bundle Contains 6 of Our Premium Courses for One Discounted Price:</strong></p><ul><li><strong> Course 01: </strong>Adobe Photoshop</li><li><strong> Course 02: </strong>Adobe Illustrator Certificate Course</li><li><strong> Course 03: </strong>Adobe Premiere Pro CC</li><li><strong> Course 04: </strong>Adobe InDesign</li><li><strong> Course 05: </strong>Adobe Lightroom CC</li><li><strong> Course 06: </strong>Adobe After Effect: Gradient Animation</li></ul> <p><strong>Learning Outcomes:</strong></p><p>By the end of this Adobe Photoshop , Adobe Illustrator, Lightroom CC , After Effect bundle course, learners will be able to:</p><ul><li>Learn to design captivating graphics with Adobe Photoshop.</li><li>Gain insights into vector design using Adobe Illustrator.</li><li>Understand non-linear video editing techniques with Premiere Pro CC.</li><li>Discover layout design with Adobe InDesign.</li><li>Learn image editing and management using Lightroom CC.</li><li>Master the creation of motion graphics with Adobe After Effects.</li><li>Become proficient in all Adobe Creative Suite tools, expanding your skillset.</li></ul> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>This Adobe Photoshop , Adobe Illustrator, Lightroom CC , After Effect course is ideal for:</p><ul><li>Aspiring Graphic Designers</li><li>Enthusiastic Content Creators</li><li>Prospective Digital Marketers</li><li>Ambitious Visual Artists</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 bundle!</p>
                        </div> <h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Graphic Designer</li><li>Video Editor</li><li>Digital Content Creator</li><li>Visual Effects Artist</li><li>Digital Marketing Specialist</li><li>Art Director</li></ul>
                        </div> <h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4> Digital certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4> Hard copy certificate</h4>
        <p>
            Hard copy certificate -
            £29
        </p>
            <div><p>If you are an international student, you will be required to pay an additional fee of <strong>10 GBP</strong> for international delivery, and <strong>4.99 GBP</strong> for delivery within the UK, for each certificate</p></div>

                        </div>

Academy for Health and Fitness

Adobe Photoshop

Adobe InDesign

Graphic Design

Adobe Illustrator

Graphic Design & Illustration

Adobe After Effects

Adobe Photoshop , Adobe Illustrator, Lightroom CC , After Effect

<strong>This course equips learners with a comprehensive understanding of the NumPy stack, including NumPy, Matplotlib, Pandas, and SciPy, to effectively tackle common challenges in deep learning and data science. Master the basics with this carefully structured course.</strong>
        <p>Welcome to the course where you will learn about the NumPy stack in Python, which is an important prerequisite for deep learning, machine learning, and data science.

In this self-paced course, you will learn how to use NumPy, Matplotlib, Pandas, and SciPy to perform critical tasks related to data science and machine learning. This involves performing numerical computation and representing data, visualizing data with plots, loading in, and manipulating data using DataFrames, performing statistics and probability, and building machine learning models for classification and regression.

In this course, we will first start with NumPy; we will understand the benefits of NumPy array and then we will look at some complicated matrix operations, such as products, inverses, determinants, and solving linear systems.

Then we will cover Matplotlib. In this section, we will go over some common plots, namely the line chart, scatter plot, and histogram. We will also look at how to show images using Matplotlib.

Next, we will talk about Pandas. We will look at how much easier it is to load a dataset using Pandas versus trying to do it manually. Then we will look at some data frame operations useful in machine learning, such as filtering by column, filtering by row, and the apply function.

Later, you will learn about SciPy. In this section, you will learn how to do common statistics calculations, including getting the PDF value, the CDF value, sampling from a distribution, and statistical testing.

Finally, we will also cover some basics of machine learning that will help us start our deep learning journey.

By the end of the course, we will be able to confidently use the NumPy stack in deep learning and data science.</p>
        <h2>What You Will Learn</h2><p>Understand supervised machine learning with real-world examples<br>Understand and code using the NumPy stack<br>Make use of NumPy, SciPy, Matplotlib, and Pandas to implement numerical algorithms<br>Understand the pros and cons of various machine learning models<br>Get a brief introduction to the classification and regression<br>Learn how to calculate the PDF and CDF under the normal distribution</p>
        <h2>Audience</h2><p>This course is designed for anyone who is interested in data science and machine learning, who knows Python and wants to take the next step into Python libraries for data science, or who is interested in acquiring tools to implement machine learning algorithms.<br><br>One must have decent Python programming skills and a basic understanding of linear algebra and probability for this course.</p>
        <h2>Approach</h2><p>You will learn how to use NumPy, Matplotlib, Pandas, and SciPy to carry out crucial data science and machine learning tasks in this self-paced course. The course is well-balanced with both theoretical and practical coding exercises. 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>Study basics of machine learning and understand how to use the NumPy stack for deep learning in data science * Learn how to use NumPy, Matplotlib, Pandas, and SciPy for critical tasks in data science and machine learning * Perform numerical computations, visualize data, load, and manipulate datasets using Pandas</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 and Logistics</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 and Outline</p><p style='margin: 0'>In this video, we will get introduced to the course and understand the learning objective.</p></td></tr></tbody></table><br><h4>2. NumPy</h4><p>In this section of the course, we will dive into the world of NumPy, a powerful numerical computing library in Python that is widely used in data science and machine learning.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. NumPy Section Introduction</p><p style='margin: 0'>In this video, we will have a quick overview of NumPy.</p></td></tr><tr><td style='padding: 15px;'><p>2. Arrays Versus Lists</p><p style='margin: 0'>In this video, we will be comparing NumPy array to Python list.</p></td></tr><tr><td style='padding: 15px;'><p>3. Dot Product</p><p style='margin: 0'>In this video, we will discuss Dot Product in NumPy.</p></td></tr><tr><td style='padding: 15px;'><p>4. Speed Test</p><p style='margin: 0'>In this video, we will do a speed test to measure how much faster NumPy is than regular Python list in terms of the dot product.</p></td></tr><tr><td style='padding: 15px;'><p>5. Matrices</p><p style='margin: 0'>In this video, you will learn about matrices.</p></td></tr><tr><td style='padding: 15px;'><p>6. Solving Linear Systems</p><p style='margin: 0'>In this video, we will work on solving an example problem to understand linear systems.</p></td></tr><tr><td style='padding: 15px;'><p>7. Generating Data</p><p style='margin: 0'>In this video, we will discuss about generatingabout how to generate data.</p></td></tr><tr><td style='padding: 15px;'><p>8. NumPy Exercise</p><p style='margin: 0'>In this video, we will look at the NumPy exercise that must be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>9. Where to Learn More NumPy</p><p style='margin: 0'>In this video, we will understand why we need NumPy and how it is applied in more advanced scenarios.</p></td></tr></tbody></table><br><h4>3. Matplotlib</h4><p>In this section, we will talk about Matplotlib.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Matplotlib Section Introduction</p><p style='margin: 0'>In this video, we will have a quick overview of Matplotlib that is used to visualize data and see what we are going to cover in this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. Line Chart</p><p style='margin: 0'>In this video, you will learn about a line chart.</p></td></tr><tr><td style='padding: 15px;'><p>3. Scatterplot</p><p style='margin: 0'>In this video, you will learn about a scatterplot.</p></td></tr><tr><td style='padding: 15px;'><p>4. Histogram</p><p style='margin: 0'>In this video, you will learn about a histogram.</p></td></tr><tr><td style='padding: 15px;'><p>5. Plotting Images</p><p style='margin: 0'>In this video, you will learn how to plot images.</p></td></tr><tr><td style='padding: 15px;'><p>6. Matplotlib Exercise</p><p style='margin: 0'>In this video, we will have a look at the Matplotlib exercise that needs to be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>7. Where to Learn More Matplotlib</p><p style='margin: 0'>In this video, we will understand from where we can learn more about Matplotlib.</p></td></tr></tbody></table><br><h4>4. Pandas</h4><p>In this section, we will talk about Pandas.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Pandas Section Introduction</p><p style='margin: 0'>In this video, we will be introduced to Pandas and understand this section''s learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. Loading in Data</p><p style='margin: 0'>In this video, you will learn how to load in data using Pandas.</p></td></tr><tr><td style='padding: 15px;'><p>3. Selecting Rows and Columns</p><p style='margin: 0'>In this video, you will learn how to select rows and columns from a data frame.</p></td></tr><tr><td style='padding: 15px;'><p>4. The apply() Function</p><p style='margin: 0'>In this video, you will learn about the apply() function.</p></td></tr><tr><td style='padding: 15px;'><p>5. Plotting with Pandas</p><p style='margin: 0'>In this video, you will learn how to plot with Pandas.</p></td></tr><tr><td style='padding: 15px;'><p>6. Pandas Exercise</p><p style='margin: 0'>In this video, we will have a look at the Pandas exercise that needs to be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>7. Where to Learn More Pandas</p><p style='margin: 0'>In this video, we will understand from where we can learn more about Pandas.</p></td></tr></tbody></table><br><h4>5. SciPy</h4><p>In this section, we will talk about SciPy.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. SciPy Section Introduction</p><p style='margin: 0'>In this video, we will be introduced to SciPy and understand this section''s learning objective.</p></td></tr><tr><td style='padding: 15px;'><p>2. PDF and CDF</p><p style='margin: 0'>In this video, you will learn how to calculate PDF and CDF under normal distribution.</p></td></tr><tr><td style='padding: 15px;'><p>3. Convolution</p><p style='margin: 0'>In this video, you will learn how to apply convolution to an image to create a blurring filter.</p></td></tr><tr><td style='padding: 15px;'><p>4. SciPy Exercise</p><p style='margin: 0'>In this video, we will have a look at the SciPy exercise that needs to be resolved on our own.</p></td></tr><tr><td style='padding: 15px;'><p>5. Where to Learn More SciPy</p><p style='margin: 0'>In this video, we will summarize what we have learnt about SciPy and understand where it can be applied.</p></td></tr></tbody></table><br><h4>6. Machine Learning Basics</h4><p>In this section, we will cover machine learning basics.</p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Machine Learning: Section Introduction</p><p style='margin: 0'>In this video, we will understand what machine learning is and the learning objective of this section.</p></td></tr><tr><td style='padding: 15px;'><p>2. What Is Classification?</p><p style='margin: 0'>In this video, we will understand classification.</p></td></tr><tr><td style='padding: 15px;'><p>3. Classification in Code</p><p style='margin: 0'>In this video, we will practice all that we have learnt in the previous video about classification.</p></td></tr><tr><td style='padding: 15px;'><p>4. What Is Regression?</p><p style='margin: 0'>In this video, we will understand what regression is.</p></td></tr><tr><td style='padding: 15px;'><p>5. Regression in Code</p><p style='margin: 0'>In this video, we will practice all that we have learnt in the previous video about regression.</p></td></tr><tr><td style='padding: 15px;'><p>6. What Is a Feature Vector?</p><p style='margin: 0'>In this video, you will learn about a feature vector.</p></td></tr><tr><td style='padding: 15px;'><p>7. Machine Learning Is Nothing but Geometry.</p><p style='margin: 0'>In this video, we will understand that machine learning is nothing but a geometry problem and see how it works for classification and regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. All Data Is the Same</p><p style='margin: 0'>In this video, we will understand how all data is the same as all machine learning interfaces are the same.</p></td></tr><tr><td style='padding: 15px;'><p>9. Comparing Different Machine Learning Models</p><p style='margin: 0'>In this video, we will understand how all machine learning interfaces are the same.</p></td></tr><tr><td style='padding: 15px;'><p>10. Machine Learning and Deep Learning: Future Topics</p><p style='margin: 0'>In this video, you will learn what other types of machine learning exist and some future topics that we can look forward to learning.</p></td></tr><tr><td style='padding: 15px;'><p>11. Machine Learning: Section Summary</p><p style='margin: 0'>In this video, we will summarize our learning from this section.</p></td></tr></tbody></table>

Pandas

Deep Learning

This course equips learners with a comprehensive understanding of the NumPy stack, including NumPy, Matplotlib, Pandas, and SciPy, to effectively tackle common challenges in deep learning and data science. Master the basics with this carefully structured course.

Data Science Prerequisites - NumPy, Matplotlib, and Pandas in Python

Study Plex

Institute of Leadership & Management (ILM)

Machine Learning Masterclass

<p>Did you know that businesses that use linear programming techniques experience an average increase in productivity of 10-15%? With statistics like these, it's no wonder that linear programming is becoming an increasingly important skill in the world of business.</p> <p>Our Level 3, 5 &amp; 7 Linear Programming course is designed to give learners a comprehensive understanding of linear programming, from the basics to advanced techniques. With this course, you will be equipped with the knowledge and skills to help businesses improve their productivity and profitability.</p> <p>What sets our course apart is that it is endorsed by the Quality Licence Scheme (QLS), an independent accrediting body that ensures the quality of our courses. Upon completion, learners will receive a<strong> Level 7 Endorsed certificate</strong> hardcopy free of charge, which is recognised by employers worldwide.</p> <p>But that's not all - we are also offering five career-guided courses for free with this QLS course, which will help you to boost your employability and take your career to the next level. These courses include Career Development Plan Fundamentals, Networking Skills for Personal Success, Boost Your Confidence and Self-Esteem, CV Writing and Job Searching, and Learn to Reduce and Manage Stress.</p> <p>With our course, you will receive top-quality education and valuable guidance, enabling you to gain the practical skills and knowledge you need to succeed in the competitive world of business. Enrol in our Level 3, 5 &amp; 7 Linear Programming course today and take the first step towards a successful career in business.</p> <p><strong>Learning Outcomes:</strong></p> <ul> <li>Understand the basic principles and concepts of linear programming, including the use of constraints and objective functions.</li><li>Learn to solve problems with mixed-type constraints, including those with multiple solutions.</li><li>Gain knowledge of the different types of constraints, including those of the '=', '&gt;=' and '&lt;=' types.</li><li>Develop the ability to simplify complex problems and identify degeneracy in linear programming.</li><li>Master the two-phase method and sensitivity analysis to evaluate solutions and identify the impact of changes in parameters.</li><li>Understand the concept of duality in linear programming and its applications.</li><li>Learn to use Solver tools and mobile apps for linear programming problems and find online solutions.</li></ul> <p><strong>Please Note: Imperial Academy is a One Education approved resale partner for Quality Licence Scheme Endorsed courses.</strong></p><p>Diploma in Level 3, 5 &amp; 7 Linear Programming at QLS at QLS's Description:</p> <p><strong>Level 3:</strong></p> <p>In this level, you will be introduced to the basics of linear programming, including the use of constraints and objective functions. You will learn how to solve problems with mixed-type constraints, including those with multiple solutions. We will also discuss the different types of constraints, including those of the '=', '&gt;=' and '&lt;=' types. You will have the opportunity to practice these concepts through various problem sets and examples.</p> <p><strong>Level 5:</strong></p> <p>In this level, you will learn how to simplify complex problems and identify degeneracy in linear programming. We will introduce you to the two-phase method and sensitivity analysis, which will help you evaluate solutions and identify the impact of changes in parameters. We will also discuss the concept of duality in linear programming and its applications. You will have the opportunity to practice these concepts through case studies and problem sets.</p> <p><strong>Level 7:</strong></p> <p>In this level, we will take a deeper dive into sensitivity analysis and duality in linear programming. You will learn how to use Solver tools and mobile apps to find online solutions for linear programming problems. We will present a production scheduling problem for formulation and an infeasible problem as case studies to help you apply these concepts in a practical context.</p> <p><strong>This bundle comprises the following courses:</strong><strong><br /></strong><strong>QLS Endorsed Course:</strong></p> <ul> <li><strong>Course 01:</strong> Advanced Diploma in Linear Programming at QLS Level 7</li></ul> <p><strong>CPD QS Accredited Courses:</strong></p> <ul> <li><strong>Course 01:</strong> Career Development Plan Fundamentals</li><li><strong>Course 02:</strong> Networking Skills for Personal Success</li><li><strong>Course 03:</strong> Boost Your Confidence and Self-Esteem</li><li><strong>Course 04:</strong> CV Writing and Job Searching</li><li><strong>Course 05:</strong> Learn to Reduce and Manage Stress</li></ul> <p><strong>Certificate</strong></p> <p>In pursuing this QLS-endorsed course, learners attain a profound understanding and amplify their professional prowess, enriching their curriculum vitae.</p> <p>The Quality Licence Scheme stands as a reputable and distinguished endorsement, emblematic of exceptional standards and excellence throughout the UK.</p> <p>Upon completion, learners will receive an Advanced Diploma in Linear Programming at QLS Level 7 Endorsed hard copy certificate, making it easy for them to showcase their achievements.</p><h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                             <ul> <li>Business professionals who want to improve their decision-making skills.</li><li>Students who want to gain a competitive advantage in the job market.</li><li>Data analysts who want to improve their knowledge of linear programming.</li><li>Operations managers who want to optimise their business processes.</li><li>Anyone interested in improving their problem-solving skills.</li></ul> 
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                             <ul> <li>Operations Research Analyst - £30,000 to £60,000</li><li>Data Analyst - £25,000 to £50,000</li><li>Business Analyst - £25,000 to £45,000</li><li>Financial Analyst - £25,000 to £50,000</li><li>Logistics Manager - £30,000 to £60,000</li><li>Supply Chain Analyst - £25,000 to £50,000</li><li>Management Consultant - £40,000 to £80,000</li></ul> 
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>Certificate of completion</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Imperial Academy

Level 7 QLS Endorsed Course | Endorsed Certificate Included | Plus 5 Career Guided Courses | CPD Accredited

Level 3, 5 & 7 Linear Programming at QLS

<strong>Get started with using linear algebra in your data science projects</strong>
        <p>Vectorizing your code is an essential skill to make your calculations faster and take advantage of the capabilities of modern machine and deep learning packages. This course will get you up and running with linear algebra fundamentals for data science in Python. 

In this course, you will learn about scalars, vectors, and matrices and the geometrical meaning of these objects. You will also learn how you should use linear algebra in your Python code. In addition to this, you'll be able to perform operations such as addition, subtraction and dot product. As you cover further sections, you'll focus on the different syntactical errors you can encounter while vectorizing your code.  

By the end of this course, you will have gained the skills you need to use linear algebra confidently in your data science projects. 

All code and supporting files for this course are available at - https://github.com/PacktPublishing/Linear-Algebra-for-Data-Science-in-Python</p>
        <h2>What You Will Learn</h2><p>Focus on the addition and subtraction of Matrix <br>Understand errors when adding matrices <br>Learn why linear algebra is useful</p>
        <h2>Audience</h2><p>This course is designed for aspiring data scientists or anyone who wants to learn linear algebra in Python.</p>
        <h2>Approach</h2><p>This course takes you through linear algebra in a systematic manner. It is even packed with step-by-step instructions and working examples to enhance your learning experience.</p>
        <h2>Key Features</h2><p>Learn linear algebra for data science and understand the essential concepts  * Understand matrix, scalars, and vectors and learn how to use them</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/linear-algebra-for-data-science-in-python</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. Linear Algebra Fundamentals</h4><p></p>

Linear Algebra

Get started with using linear algebra in your data science projects

Linear Algebra for Data Science in Python

<div>
        <div>
            <h2>
                Recognised Accreditation
            </h2>
            <p>This course is accredited by continuing professional development (CPD). CPD UK is globally
                recognised by employers, professional organisations, and academic institutions, thus a
                certificate from CPD Certification Service creates value towards your professional goal
                and achievement.</p>

                

                <p>The Quality Licence Scheme is a brand of the Skills and Education Group, a leading national awarding organisation for providing high-quality vocational qualifications across a wide range of industries.</p>
                
    </div>
    <div>
        <div>
            <div>
                
                <h3>What is CPD?</h3>
                <p>Employers, professional organisations, and academic institutions all recognise CPD,
                    therefore a credential from CPD Certification Service adds value to your professional
                    goals and achievements.</p>
                <h4>Benefits of CPD</h4>
                <ul>
                    <li>Improve your employment prospects </li>
                    <li>Boost your job satisfaction </li>
                    <li>Promotes career advancement </li>
                    <li>Enhances your CV </li>
                    <li>Provides you with a competitive edge in the job market </li>
                    <li>Demonstrate your dedication </li>
                    <li>Showcases your professional capabilities</li>
                </ul>
            </div>
        </div>
        <div>
            <div>
                
                <h3>What is IPHM? </h3>
                <p>The IPHM is an Accreditation Board that provides Training Providers with international
                    and global accreditation. The Practitioners of Holistic Medicine (IPHM) accreditation
                    is a guarantee of quality and skill. </p>
                <h4>Benefits of IPHM </h4>
                <ul>
                    <li>It will help you establish a positive reputation in your chosen field </li>
                    <li>You can join a network and community of successful therapists that are dedicated to
                    providing excellent care to their client </li>
                    <li>You can flaunt this accreditation in your CV </li>
                    <li>It is a worldwide recognised accreditation </li>
                </ul>
            </div>
        </div>
        <div>
            <div>
                
                <h3>What is Quality Licence Scheme?</h3>
                <p>This course is endorsed by the Quality Licence Scheme for its high-quality,
                    non-regulated provision and training programmes. The Quality Licence Scheme is a brand
                    of the Skills and Education Group, a leading national awarding organisation for
                    providing high-quality vocational qualifications across a wide range of industries.</p>
                <h4> Benefits of Quality License Scheme </h4>
                <ul>
                    <li>Certificate is valuable</li>
                    <li>Provides a competitive edge in your career</li>
                    <li>It will make your CV stand out</li>
                </ul>
            </div>
        </div>
    </div>
                    </div><div>
				
<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table>				<tbody><tr>
					<td colspan="4">Getting Data Ready for Regression Model</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Transportation Problem in Excel using Goal Seek					</td>

					
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Gathering Business Knowledge					</td>

					
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Data Exploration					</td>

					
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						The Data and the Data Dictionary					</td>

					
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Univariate analysis and EDD					</td>

					
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Discriptive Data Analytics in Excel					</td>

					
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Outlier Treatment					</td>

					
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Identifying and Treating Outliers in Excel					</td>

					
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Missing Value Imputation					</td>

					
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Identifying and Treating missing values in Excel					</td>

					
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Variable Transformation in Excel					</td>

					
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Dummy variable creation: Handling qualitative data					</td>

					
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Dummy Variable Creation in Excel					</td>

					
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Correlation Analysis					</td>

					
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Creating Correlation Matrix in Excel					</td>

					
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Creating Regression Model</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						The Problem Statement					</td>

					
					<td> </td>
					<td><span><i></i> 00:01:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Basic Equations and Ordinary Least Squares (OLS) method					</td>

					
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Assessing accuracy of predicted coefficients					</td>

					
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Assessing Model Accuracy: RSE and R squared					</td>

					
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Creating Simple Linear Regression model					</td>

					
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Multiple Linear Regression					</td>

					
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						The F - statistic					</td>

					
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Interpreting results of Categorical variables					</td>

					
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Creating Multiple Linear Regression model					</td>

					
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td colspan="4">What-if analysis</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Excel: Running Linear Regression using Solver					</td>

					
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Assessment</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assessment - Linear Regression Analysis In MS Excel</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Certificate of Achievement</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Certificate of Achievement					</td>

					
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Get Your Insurance Now</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Get Your Insurance Now					</td>

					
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Feedback</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>
						Feedback					</td>

					
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

Linear Regression Analysis in Microsoft Excel

<strong>This course covers the fundamental topics of statistical methodology, enabling you to understand the application and interpretation of linear and non-linear regression modeling.</strong>
        <p>Working with statistics and quantitative reports requires a good understanding of statistics fundamentals and techniques. However, learning and applying new statistical techniques can often be a daunting experience. This is where this course comes into play.
To make your experience with statistics a pleasant one, this course gives you comprehensive knowledge of basic principles of the statistical methodology, focusing on linear regression and non-linear regression.
The course starts with an introduction to easy statistics and gives you an overview of the course objectives. Next, you will explore the types of regression analysis that exist and find out how ordinary least squares (OLS) works. To gain a deeper understanding of linear regression and OLS, you will learn to interpret and analyze complicated regression output from OLS. You will also focus on Gauss-Markov assumptions and zero conditional mean. Moving ahead, you will cover non-linear regression, exploring how it works, what the different non-linear regression models are, and the major uses. Towards the end, you will learn to work around with regression modeling with the help of practical examples.
By the end of this video, you will be well-versed with linear and non-linear regression and the basic principles of the statistical methodology.

All the resource files are added on GitHub at https://github.com/PacktPublishing/-Easy-Statistics-Linear-and-Non-Linear-Regression</p>
        <h2>What You Will Learn</h2><p>Understand the basic concept of statistical regression analysis<br>Become familiar with linear and non-linear regression terminologies<br>Distinguish between different types of regression methods<br>Analyze and integrate complicated regression output from ordinary least squares (OLS)<br>Find out the difference between logit and probit transformation<br>Model non-linear relationships in a linear regression</p>
        <h2>Audience</h2><p>If you're a student, an experienced professional, a manager, or a government worker who wants to learn linear and non-linear regression, regression modeling, and ordinary least squares, then this course is for you. This is a beginner-level course and does not require any prior knowledge of mathematics or statistics.</p>
        <h2>Approach</h2><p>With the help of eye-catching animated graphics and interesting examples, this course will teach you linear and non-linear regression and regression modeling to ensure that you become well-versed with the fundamentals of statistics and quantitative analysis.</p>
        <h2>Key Features</h2><p>Understand the statistical fundamentals of ordinary least squares (OLS) * Gain the confidence to comfortably interpret complicated regression output from OLS * Explore regression modeling and its application</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/-Easy-Statistics-Linear-and-Non-Linear-Regression</p>
        <h2>About the Author</h2><h3>Franz Buscha</h3><p>Franz Buscha is a professor of economics at the University of Westminster, which he joined after completing his Ph.D. in economics at Lancaster University. He has been involved in numerous funded research projects from research councils and government departments. He has also contributed to a wide range of projects, including policy evaluation and bespoke econometric advice to UK government departments. Franz has published in leading journals and contributed to numerous policy reports. His research has even been covered by various media outlets. He is an experienced online educator and has published several online courses, including LinkedIn Learning. Franz also has a monthly radio program called Policy Matters on Share Radio.</p>
        <h2>Course Outline</h2><h4>1. Linear Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is Easy Statistics: Linear Regression?</p><p style='margin: 0'>This video explains the concept of easy statistics with linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Linear Regression?</p><p style='margin: 0'>This video explains the concept of linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>3. Learning Outcomes</p><p style='margin: 0'>This video explains the various learning outcomes.</p></td></tr><tr><td style='padding: 15px;'><p>4. Whom is this Course for?</p><p style='margin: 0'>This video discusses the target audience for this course.</p></td></tr><tr><td style='padding: 15px;'><p>5. Prerequisites</p><p style='margin: 0'>This video explains the various prerequisites of the course.</p></td></tr><tr><td style='padding: 15px;'><p>6. Using Stata</p><p style='margin: 0'>This video shows how to use Stata for linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>7. What is Regression Analysis?</p><p style='margin: 0'>This video explains the concept of regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>8. Get to Know About Linear Regression</p><p style='margin: 0'>This video explains the concept of linear regression in detail.</p></td></tr><tr><td style='padding: 15px;'><p>9. Why is Regression Analysis Useful?</p><p style='margin: 0'>This video explains the importance and uses of regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>10. What Types of Regression Analysis Exist?</p><p style='margin: 0'>This video explains the various types of regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>11. Explaining Regression</p><p style='margin: 0'>This video explains the concept of regression.</p></td></tr><tr><td style='padding: 15px;'><p>12. Lines of Best Fit</p><p style='margin: 0'>This video explains the lines of best fit concept.</p></td></tr><tr><td style='padding: 15px;'><p>13. Causality vs. Correlation</p><p style='margin: 0'>This video illustrates the difference between causality and correlation.</p></td></tr><tr><td style='padding: 15px;'><p>14. What is Ordinary Least Squares (OLS)?</p><p style='margin: 0'>This video explains the concept of OLS.</p></td></tr><tr><td style='padding: 15px;'><p>15. Ordinary Least Squares (OLS) Visual - Part 1</p><p style='margin: 0'>This video is the first part of the two-part video that explains OLS visually.</p></td></tr><tr><td style='padding: 15px;'><p>16. Ordinary Least Squares (OLS) Visual - Part 2</p><p style='margin: 0'>This video is the second part of the two-part video that explains OLS visually.</p></td></tr><tr><td style='padding: 15px;'><p>17. Sum of Squares</p><p style='margin: 0'>This video explains how to calculate the sum of squares using linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>18. Best Linear Unbiased Estimator</p><p style='margin: 0'>This video explains the best linear unbiased estimator.</p></td></tr><tr><td style='padding: 15px;'><p>19. The Gauss-Markov Assumptions</p><p style='margin: 0'>This video discusses the Gauss-Markov assumptions.</p></td></tr><tr><td style='padding: 15px;'><p>20. Homoskedasticity</p><p style='margin: 0'>This video explains the concept of homoskedasticity.</p></td></tr><tr><td style='padding: 15px;'><p>21. No Perfect Collinearity</p><p style='margin: 0'>This video explains the concept of no perfect collinearity.</p></td></tr><tr><td style='padding: 15px;'><p>22. Linear in Parameters</p><p style='margin: 0'>This video focuses on linearity in parameters.</p></td></tr><tr><td style='padding: 15px;'><p>23. Zero Conditional Mean</p><p style='margin: 0'>This video explains what is zero conditional mean.</p></td></tr><tr><td style='padding: 15px;'><p>24. How to Test and Correct for Endogeneity?</p><p style='margin: 0'>This video explains how to test and correct for endogeneity.</p></td></tr><tr><td style='padding: 15px;'><p>25. The Gauss-Markov Assumptions - Recap</p><p style='margin: 0'>This video presents a recap of the Gauss-Markov assumptions.</p></td></tr><tr><td style='padding: 15px;'><p>26. Stata - Applied Examples</p><p style='margin: 0'>This video explains with an example how to use Stata.</p></td></tr><tr><td style='padding: 15px;'><p>27. Final Thoughts and Tips</p><p style='margin: 0'>This video provides some final thoughts and tips with respect to linear regression.</p></td></tr></tbody></table><br><h4>2. Non-Linear Regression</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. What is Easy Statistics: Non-Linear Regression?</p><p style='margin: 0'>This video explains the concept of easy statistics with non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>2. What is Non-Linear Regression?</p><p style='margin: 0'>This video explains the concept of non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>3. What are the Main Learning Outcomes?</p><p style='margin: 0'>This video presents the learning outcome of this course.</p></td></tr><tr><td style='padding: 15px;'><p>4. Whom is this Course for?</p><p style='margin: 0'>This video discusses the target audience for this course.</p></td></tr><tr><td style='padding: 15px;'><p>5. Prerequisites</p><p style='margin: 0'>This video discusses the various prerequisites for this course.</p></td></tr><tr><td style='padding: 15px;'><p>6. Using Stata</p><p style='margin: 0'>This video explains how to use Stata.</p></td></tr><tr><td style='padding: 15px;'><p>7. What is Non-Linear Regression Analysis?</p><p style='margin: 0'>This video explains the concept of non-linear regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>8. How does Non-Linear Regression Work?</p><p style='margin: 0'>This video explains the working of non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>9. Why is Non-Linear Regression analysis Useful?</p><p style='margin: 0'>This video explains the importance and uses of non-linear regression analysis.</p></td></tr><tr><td style='padding: 15px;'><p>10. Types of Non-Linear Regression models</p><p style='margin: 0'>This video explains the various types of non-linear regression models.</p></td></tr><tr><td style='padding: 15px;'><p>11. Maximum Likelihood</p><p style='margin: 0'>This video explains the maximum likelihood estimation technique.</p></td></tr><tr><td style='padding: 15px;'><p>12. Linear Probability Model</p><p style='margin: 0'>This video explains the linear probability model.</p></td></tr><tr><td style='padding: 15px;'><p>13. The Logit and Probit Transformation</p><p style='margin: 0'>This video explains the logit and probit transformation model.</p></td></tr><tr><td style='padding: 15px;'><p>14. Latent Variables</p><p style='margin: 0'>This video focuses on latent variables.</p></td></tr><tr><td style='padding: 15px;'><p>15. What are Marginal Effects?</p><p style='margin: 0'>This video explains the concept of marginal effects.</p></td></tr><tr><td style='padding: 15px;'><p>16. Dummy Explanatory Variables</p><p style='margin: 0'>This video focuses on the dummy explanatory variables.</p></td></tr><tr><td style='padding: 15px;'><p>17. Multiple Non-Linear Regression</p><p style='margin: 0'>This video explains multiple non-linear regression.</p></td></tr><tr><td style='padding: 15px;'><p>18. Goodness-of-Fit</p><p style='margin: 0'>This video discusses the concept of goodness-of-fit.</p></td></tr><tr><td style='padding: 15px;'><p>19. A Note about Logit Coefficients</p><p style='margin: 0'>This video provides a note about logit coefficients.</p></td></tr><tr><td style='padding: 15px;'><p>20. Tips for Logit and Probit Regression</p><p style='margin: 0'>This video provides some tips for logit and probit regression.</p></td></tr><tr><td style='padding: 15px;'><p>21. Back to the Linear Probability Model</p><p style='margin: 0'>This video explains the linear probability model.</p></td></tr><tr><td style='padding: 15px;'><p>22. Stata - Applied Logit and Probit Examples</p><p style='margin: 0'>This video provides examples of applied logit and probit using Stata.</p></td></tr></tbody></table><br><h4>3. Regression Modelling</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Introduction</p><p style='margin: 0'>This video provides an introduction to regression modelling.</p></td></tr><tr><td style='padding: 15px;'><p>2. Regression Modelling - Don't Rush it</p><p style='margin: 0'>This video explains the concept of regression modelling in detail.</p></td></tr><tr><td style='padding: 15px;'><p>3. Non-Linear Shapes in Regression</p><p style='margin: 0'>This video explains the concept of non-linear shapes in regression.</p></td></tr><tr><td style='padding: 15px;'><p>4. Non-Linear Shapes in Regression - Practical Examples</p><p style='margin: 0'>This video explains the concept of non-linear shapes in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>5. How to use and Interpret Interaction Effects?</p><p style='margin: 0'>This video explains how to use and interpret interaction effects.</p></td></tr><tr><td style='padding: 15px;'><p>6. How to use and Interpret Interaction Effects? - Practical Examples</p><p style='margin: 0'>This video explains how to use and interpret interaction effects with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>7. Using Time in Regression</p><p style='margin: 0'>This video explains how to use time in regression.</p></td></tr><tr><td style='padding: 15px;'><p>8. Using Time in Regression - Practical Examples</p><p style='margin: 0'>This video explains how to use time in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>9. Categorical Explanatory Variables in Regression</p><p style='margin: 0'>This video explains the categorical explanatory variables in regression.</p></td></tr><tr><td style='padding: 15px;'><p>10. Categorical Explanatory Variables in Regression - Practical Examples</p><p style='margin: 0'>This video explains the categorical explanatory variables in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>11. Dealing with Multicollinearity in Regression</p><p style='margin: 0'>This video explains how to deal with multicollinearity in regression.</p></td></tr><tr><td style='padding: 15px;'><p>12. Dealing with Multicollinearity in Regression - Practical Examples</p><p style='margin: 0'>This video explains how to deal with multicollinearity in regression with the help of examples.</p></td></tr><tr><td style='padding: 15px;'><p>13. Dealing with Missing Data in Regression</p><p style='margin: 0'>This video explains how to deal with missing data in regression.</p></td></tr><tr><td style='padding: 15px;'><p>14. Dealing with Missing Data in Regression - Practical Examples</p><p style='margin: 0'>This video explains how to deal with missing data in regression with the help of examples.</p></td></tr></tbody></table>

Stata

Statistics

This course covers the fundamental topics of statistical methodology, enabling you to understand the application and interpretation of linear and non-linear regression modeling.

Easy Statistics: Linear and Non-Linear Regression

<strong>This course covers the fundamental topics of statistical methodology, linear regression, and ordinary least squares that every statistician needs to know.</strong>
        <p>To work with statistics and quantitative reports, you need to have a good understanding of the fundamentals and techniques of statistics. However, learning and applying new statistical techniques can often be a daunting experience and this is where this video course comes in handy. This course is designed to guide you through the basic principles of statistical methodology in a step-by-step way.
You'll start with an introduction to the course along with an overview of the learning objectives. Next, you'll explore the types of regression analysis that exist, and find out how ordinary least squares (OLS) work. To get a deeper understanding of linear regression and OLS, you'll interpret and analyze complicated regression output from OLS. Finally, you'll discover essential tips and techniques that'll help you to confidently perform regression analysis.
By the end of this video, you'll be well-versed with linear regression techniques and the basic principles of statistical methodology.
The code and resources for this course is available at https://github.com/PacktPublishing/Easy-Statistics-Linear-Regression</p>
        <h2>What You Will Learn</h2><p>Understand the basic concept of statistical regression analysis<br>Become familiar with linear regression terminologies<br>Distinguish between different kinds of regression methods<br>Find out the difference between correlation and causation<br>Analyze and integrate complicated regression output from OLS<br>Explore tips and tricks relating to regression analysis</p>
        <h2>Audience</h2><p>If you are a student at any level, an experienced professional, a manager, or a government worker who wants to delve into statistical regression or work with quantitative reports and policy analysis, then this course is for you. As the course is for beginners, it doesn't require you to have any knowledge of mathematics or statistics.</p>
        <h2>Approach</h2><p>With the help of animated graphics and interesting examples, this course will teach you the fundamentals of statistical regression terminologies.</p>
        <h2>Key Features</h2><p>Explore the basic statistical fundamentals of ordinary least squares (OLS) * Understand linear regression and its application * Gain the confidence to interpret complicated regression output</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Easy-Statistics-Linear-Regression</p>
        <h2>About the Author</h2><h3>Franz Buscha</h3><p>Franz Buscha is a professor of economics at the University of Westminster, which he joined after completing his Ph.D. in economics at Lancaster University. He has been involved in numerous funded research projects from research councils and government departments. He has also contributed to a wide range of projects, including policy evaluation and bespoke econometric advice to UK government departments. Franz has published in leading journals and contributed to numerous policy reports. His research has even been covered by various media outlets. He is an experienced online educator and has published several online courses, including LinkedIn Learning. Franz also has a monthly radio program called Policy Matters on Share Radio.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. What is Regression?</h4><p></p><br><h4>3. What is Ordinary Least Squares?</h4><p></p><br><h4>4. Ordinary Least Squares Regression in Practice</h4><p></p><br><h4>5. Tips and Final Thoughts </h4><p></p>

This course covers the fundamental topics of statistical methodology, linear regression, and ordinary least squares that every statistician needs to know.

Easy Statistics: Linear Regression

SkillWise

Advanced Linear Programming

<p><strong></strong><strong> Our Aim Is Your Satisfaction! Offer Ends Soon; Hurry Up!! </strong><strong></strong></p><p><strong>Are you looking to improve your current abilities or make a career move? Our unique</strong><strong> Statistical Concepts in R</strong><strong> course might help you get there! Expand your expertise with high-quality training - study the</strong><strong> Statistical Concepts in R </strong><strong>course and get an expertly designed, great-value training experience. Learn from industry professionals and quickly equip yourself with the specific knowledge and skills you need to excel in your chosen career through the</strong><strong> Statistical Concepts in R </strong><strong>online training course.</strong></p><p>The Statistical Concepts in R course is broken down into several in-depth modules to provide you with the most convenient and rich learning experience possible. Upon successful completion of the Statistical Concepts in R course, an instant e-certificate will be exhibited in your profile that you can order as proof of your skills and knowledge. Add these amazing new skills to your resume and boost your employability by simply enrolling in this course.</p><p>This Statistical Concepts in R training can help you to accomplish your ambitions and prepare you for a meaningful career. So, join us today and gear up for excellence!</p><p><strong>Why Prefer Us? </strong></p><ul><li>Opportunity to earn a certificate accredited by <strong>CPDQS.</strong></li><li><strong>Get a free student ID </strong>card! (£10 postal charge will be applicable for international delivery)</li><li>Innovative and Engaging Content.</li><li>Free Assessments</li><li>24/7 Tutor Support.</li></ul> <p>Take a step toward a brighter future!</p><p><strong>*** Course Curriculum ***</strong></p><p><strong>Module 01: Introduction to the Course</strong></p><ul><li>Introduction</li></ul><p><strong>Module 02: Simple Linear Regression</strong></p><ul><li>Install R, RStudio and Basic Functionality</li><li>Basics of Linear Regression</li><li>Basics of Linear Regression continued</li></ul><p><strong>Module 03: Linear Regression Analysis</strong></p><ul><li>Linear Relationships</li><li>Line of Best Fit, SSE and MSE</li><li>Linear Regression Analysis Continued</li><li>Regression Results and Interpretation</li><li>Predicting Future Profits</li><li>Statistical Validity Tests</li><li>Statistical Validity Discussion</li></ul><p><strong>Module 04: Multiple Linear Regression</strong></p><ul><li>Multiple Linear Regression</li><li>Importing the data</li><li>Correlation Matrix and MLR</li><li>MLR Results and ANOVA</li><li>The Best Model?</li><li>Interaction Terms and Validity Testing</li><li>ANOVA and Predictions</li></ul><p><strong>Module 05: Non-linear Regression</strong></p><ul><li>Non-linear Regression (and Recap)</li><li>Logistic Regression Overview</li><li>Logistic Regression: Odds, Logs and Poisson</li><li>Logistic Regression: Fitting the Models in R</li></ul><p><strong>Assessment Process</strong></p><p>Your skills and knowledge will be tested with an <strong>automated multiple-choice assessment</strong>. You will then receive instant results to let you know if you have successfully passed the Statistical Concepts in R course.</p> <h2>
        
        <div>CPD</div>
    </h2>

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

                        <div id="who_is_this_course_for">
                            <p>Anyone interested in learning more about the topic is advised to take this Statistical Concepts in R course. This course is open to everybody.</p>
                        </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>
                        </div>

 Our Aim Is Your Satisfaction! Offer Ends Soon; Hurry Up!! 
Are you looking to improve your current abilities or make a career move? Our unique Statistical Concepts in R course might help you get there! Expand your expertise with high-quality training - study the Statistical Concepts in R course and get an expertly designed, great-value training experience. Learn from industry professionals and quickly equip yourself with the specific knowledge and skills you need to excel in your chosen career through the Statistical Concepts in R online training course.
The Statistical Concepts in R course is broken down into several in-depth modules to provide you with the most convenient and rich learning experience possible. Upon successful completion of the Statistical Concepts in R course, an instant e-certificate will be exhibited in your profile that you can order as proof of your skills and knowledge. Add these amazing new skills to your resume and boost your employability by simply enrolling in this course.
This Statistical Concepts in R training can help you to accomplish your ambitions and prepare you for a meaningful career. So, join us today and gear up for excellence!
Why Prefer Us? 
Opportunity to earn a certificate accredited by CPDQS.
Get a free student ID card! (£10 postal charge will be applicable for international delivery)
Innovative and Engaging Content.
Free Assessments
24/7 Tutor Support.
Take a step toward a brighter future!
*** Course Curriculum ***
Module 01: Introduction to the Course
Introduction
Module 02: Simple Linear Regression
Install R, RStudio and Basic Functionality
Basics of Linear Regression
Basics of Linear Regression continued
Module 03: Linear Regression Analysis
Linear Relationships
Line of Best Fit, SSE and MSE
Linear Regression Analysis Continued
Regression Results and Interpretation
Predicting Future Profits
Statistical Validity Tests
Statistical Validity Discussion
Module 04: Multiple Linear Regression
Multiple Linear Regression
Importing the data
Correlation Matrix and MLR
MLR Results and ANOVA
The Best Model?
Interaction Terms and Validity Testing
ANOVA and Predictions
Module 05: Non-linear Regression
Non-linear Regression (and Recap)
Logistic Regression Overview
Logistic Regression: Odds, Logs and Poisson
Logistic Regression: Fitting the Models in R
Assessment Process
Your skills and knowledge will be tested with an automated multiple-choice assessment. You will then receive instant results to let you know if you have successfully passed the Statistical Concepts in R course.
 CPD 
10 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
Anyone interested in learning more about the topic is advised to take this Statistical Concepts in R course. This course is open to everybody.
 Requirements 
You will not need any prior background or expertise to enrol in this course.
 Career path 
After completing this course, you are to start your career or begin the next phase of your career.

Statistical Computing

Statistical Concepts in R

<p fr-original-style="" style="color: var(--text); font-size: var(--font-size); box-sizing: border-box;">In this workshop we address Bereavement.&nbsp;Grief is a complex emotion that stems from various losses, not just death. It encompasses feelings of loss from relationships, dreams, health, and more. Losing a partner or a family member can profoundly impact one&#39;s life and support system. Children face unique challenges in grieving, which require careful support. The grieving process is unpredictable and non-linear, involving stages like denial, anger, and acceptance. Hypnotherapy offers a therapeutic approach to address emotional processing, trauma, and acceptance, aiding individuals in navigating their grief journey. While grief is universal, seeking support and exploring therapeutic options like hypnotherapy can help individuals find healing and solace amidst their pain.</p>

In this workshop we address Bereavement. Grief is a complex emotion that stems from various losses, not just death. It encompasses feelings of loss from relationships, dreams, health, and more. Losing a partner or a family member can profoundly impact one's life and support system. Children face unique challenges in grieving, which require careful support. The grieving process is unpredictable and non-linear, involving stages like denial, anger, and acceptance. Hypnotherapy offers a therapeutic approach to address emotional processing, trauma, and acceptance, aiding individuals in navigating their grief journey. While grief is universal, seeking support and exploring therapeutic options like hypnotherapy can help individuals find healing and solace amidst their pain.

The Northern College Of Clinical Hypnotherapy

Grief Healing

Bereavement

Hypnotherapy offers a therapeutic approach to address emotional processing, trauma, and acceptance, aiding individuals in navigating their grief journey. While grief is universal, seeking support and exploring therapeutic options like hypnotherapy can help individuals find healing and solace amidst their pain.

Module 7 Part 2 Bereavement & working with loss 

Probability

Start your data science journey with this carefully constructed comprehensive course and get hands-on experience with Python for data science. Gain in-depth knowledge about core Python and essential mathematical concepts in linear algebra, probability, and statistics. Complete data science training with 13+ hours of content.

Data Science 101: Methodology, Python, and Essential Math

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			<h2>Overview
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					<p><span>This comprehensive course on <strong>Algebra Fundamentals </strong></span>will deepen your understanding on this topic.</p><p><span><span>After successful completion of this course you can acquire the required skills in this sector. This <strong>Algebra Fundamentals</strong></span> comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.</span></p><p><span><span>So enrol in this course today to fast track your career ladder.</span></span></p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p><span>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.</span></p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p><span>There is no experience or previous qualifications required for enrolment on this <strong>Algebra Fundamentals</strong></span>. It is available to all students, of all academic backgrounds.</p>					</div>
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			<h2>Requirements</h2>		</div>
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					<p><span>Our <strong>Algebra Fundamentals</strong></span><strong> </strong>is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</p><p><span><span>There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></span></p>					</div>
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					<p><span>Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to-</span></p><ul><li>Open doors of opportunities</li><li>Increase your adaptability</li><li>Keep you relevant</li><li>Boost confidence</li></ul><p>And much more!</p>					</div>
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					</div> <h3>Course Curriculum</h3><div><p>17 sections • 83 lectures • 11:02:00 total length</p></div><div><div><div><div><div><div><div><p>•Lecture 1 Introduction: 00:03:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 2 What is Algebra: 00:02:00</p></div><div><div><p>•Lecture 3 Simple Equations: 00:05:00</p></div><div><div><p>•Lecture 4 What are Polynomials: 00:04:00</p></div><div><div><p>•Lecture 5 Terms in Polynomials: 00:03:00</p></div><div><div><p>•Lecture 6 Degree of Polynomials: 00:05:00</p></div><div><div><p>•Lecture 7 Writing statements to algebraic form: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 8 Integers and common mistakes in solving integers: 00:13:00</p></div><div><div><p>•Lecture 9 Arrangement of Terms: 00:07:00</p></div><div><div><p>•Lecture 10 Powers on integers: 00:04:00</p></div><div><div><p>•Lecture11 Simplification using BODMAS: 00:08:00</p></div><div><div><p>•Lecture 12 Distributive Properties in Polynomials: 00:04:00</p></div><div><div><p>•Lecture 13 Simplify Polynomials: 00:10:00</p></div><div><div><p>•Lecture 14 Additions of Polynomials: 00:06:00</p></div><div><div><p>•Lecture 15 Subtractions of Polynomials: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 16 The rules of Indices in algebra: 00:11:00</p></div><div><div><p>•Lecture 17 Fractional indices: 00:10:00</p></div><div><div><p>•Lecture 18 Understanding indices (practice questions): 00:07:00</p></div><div><div><p>•Lecture 19 Problems from IGCSE Last year papers: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 20 Multiplication of monomial to Polynomial: 00:09:00</p></div><div><div><p>•Lecture 21 Multiplication of Polynomial by Polynomial: 00:06:00</p></div><div><div><p>•Lecture 22 Division of algebraic expression by a monomial: 00:08:00</p></div><div><div><p>•Lecture 23 Division of algebraic expression by another polynomial: 00:09:00</p></div><div><div><p>•Lecture 24 Division of a polynomial by another polynomial with remainder: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 25 Rules of brackets: 00:04:00</p></div><div><div><p>•Lecture 26 Simplification by removing brackets: 00:11:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 27 Simplification of algebraic fractions: 00:07:00</p></div><div><div><p>•Lecture 28 Rules to solve linear equations in one variable: 00:03:00</p></div><div><div><p>•Lecture 29 Solving linear equations in one variable: 00:07:00</p></div><div><div><p>•Lecture 30 Solving complex linear equations in one variable: 00:10:00</p></div><div><div><p>•Lecture 31 Word problems on linear equations in one variable: 00:13:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 32 What are Identities?: 00:05:00</p></div><div><div><p>•Lecture 33 Identity ( a + b ) Â²: 00:13:00</p></div><div><div><p>•Lecture 35 Identity aÂ² - bÂ² = (a-b) (a +b ) new: 00:07:00</p></div><div><div><p>•Lecture 36 -- Standard Identities ( a + b + c ) Â² = a Â² + b Â² + c Â² + 2 a b + 2 a c +2 b c old: 00:07:00</p></div><div><div><p>•Lecture 37 Identity (x + a) (x + b) Identity Derivation &amp; Application new: 00:08:00</p></div><div><div><p>•Lecture 38 Pascal's Triangle _ Identity ( a + b ) Â³ new: 00:07:00</p></div><div><div><p>•Lecture 39 Identities( a - b ) Â³, ( a Â³ + b Â³) and (a Â³ - b Â³) new: 00:13:00</p></div><div><div><p>•Lecture 40 - Standard Identities a Â³ + b Â³ + c Â³ - 3 a b c: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 41 -Changing the subject of formula: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 42 - Linear Inequalities: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 43 - Factorization by taking out common factor: 00:10:00</p></div><div><div><p>•Lecture 44 - Factorization by grouping the terms: 00:09:00</p></div><div><div><p>•Lecture 45 - factorize using identity a Â² - b Â²: 00:07:00</p></div><div><div><p>•Lecture 46 - factorize using identity (a + b )Â² and (a - b )Â² (2): 00:08:00</p></div><div><div><p>•Lecture 47 - factorize using identity ( a + b + c ) Â²: 00:05:00</p></div><div><div><p>•Lecture 48 - factorization by middle term split: 00:12:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 49 -Simplification of algebraic fractions: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 50 All that you need to know about co ordinate axis: 00:04:00</p></div><div><div><p>•Lecture 51 Some important facts needed to draw line graph: 00:03:00</p></div><div><div><p>•Lecture 52 - How to draw a line graph on coordinate plane: 00:03:00</p></div><div><div><p>•Lecture 53 Drawing line graphs: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 54 Simultaneous Linear Equations in two variables- intro: 00:03:00</p></div><div><div><p>•Lecture 55 Graphical method of solving linear equations: 00:06:00</p></div><div><div><p>•Lecture 56 Graphical method - more problems: 00:10:00</p></div><div><div><p>•Lecture 57 Method of Elimination by substitution: 00:09:00</p></div><div><div><p>•Lecture 58 Method of Elimination by Equating coefficients: 00:11:00</p></div><div><div><p>•Lecture 59 Method of Elimination by cross multiplication: 00:07:00</p></div><div><div><p>•Lecture 60 Equations reducible to simultaneous linear equations: 00:12:00</p></div><div><div><p>•Lecture 61 Word Problems on Linear equations: 00:18:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 62 Polynomials and Zeros of polynomials: 00:10:00</p></div><div><div><p>•Lecture 63 Remainder Theorem: 00:04:00</p></div><div><div><p>•Lecture 64 Factor Theorem: 00:08:00</p></div><div><div><p>•Lecture 65 Practice problems on Remainder and Factor Theorem: 00:09:00</p></div><div><div><p>•Lecture 66 Factorization using factor Theorem: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture 67 Zeros of polynomials Î±, Î² &amp; Î³: 00:10:00</p></div><div><div><p>•Lecture 68 Relation between zeros and coefficients of a polynomials: 00:13:00</p></div><div><div><p>•Lecture 69 Finding polynomials if zeros are known: 00:06:00</p></div><div><div><p>•Lecture 70 Practice problems on zeros of polynomials: 00:10:00</p></div><div><div><p>•Lecture 71Problems solving with Î± and Î² (part 1): 00:11:00</p></div><div><div><p>•Lecture 72 Problems solving with Î± and Î² (part 2): 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Lecture73 what are Quadratic equations: 00:03:00</p></div><div><div><p>•Lecture 74 Solutions by factorization method: 00:12:00</p></div><div><div><p>•Lecture 75 Solutions by completing square formula: 00:06:00</p></div><div><div><p>•Lecture 76 Deriving Quadratic formula: 00:05:00</p></div><div><div><p>•Lecture 77 Practice problems by Quadratic formula: 00:07:00</p></div><div><div><p>•Lecture 78 Solving complex quadratic equations by Quadratic Formula: 00:11:00</p></div><div><div><p>•Lecture 79 Solutions of reducible to Quadratic Formula: 00:09:00</p></div><div><div><p>•Lecture 80 Skilled problems on Quadratic Equations: 00:07:00</p></div><div><div><p>•Lecture 81 Exponential problems reducible to Quadratic Equations: 00:06:00</p></div><div><div><p>•Lecture 82 Nature of Roots of Quadratic Equations: 00:09:00</p></div><div><div><p>•Lecture 83 Word problems on quadratic Equations Part 1: 00:13:00</p></div><div><div><p>•Lecture 84 Word problems on quadratic Equations Part 2: 00:11:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

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