Mathematics for Data Science and Machine Learning using R

Video description

Learn the basic math for Data Science, AI, and ML using R

About This Video

  • Understand linear algebra - scalars, vectors, and matrices
  • Discover the fundamental mathematics for data science, AI, and ML using R

In Detail

With data increasing every day, Data Science has become one of the most essential aspects in most fields. From healthcare to business, data is essential everywhere. However, it revolves around three major aspects: data itself, foundational concepts, and programming languages that interpret data.

This course teaches you everything you need to know about the basic math for Data Science via the R programming language, developed specifically to perform statistics and data analytics and utilize graphical modules more effectively.

Data Science has become an interdisciplinary field that deals with the processes and systems used to extract knowledge or make predictions from large amounts of data. From helping brands to understand their customers to solve complex IT problems, its usability in almost every other field makes it very important for the functioning and growth of organizations or companies. We supply an overview of Machine Learning and the R programming language, linear algebra- scalars, vectors, matrices, linear regression, calculus-tangents, derivatives, vector calculus, vector spaces, Gradient Descent, and others.

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Table of contents

  1. Chapter 1 : Introduction
    1. Intro 00:01:02
  2. Chapter 2 : Overview of R
    1. Introduction 00:01:55
    2. Overview of R Workspace & Basic Commands 00:22:51
    3. LAB 1 Intro 00:02:28
    4. LAB 1 Solution 00:11:11
  3. Chapter 3 : Linear Algebra
    1. Scalars Vectors and Matrices 00:12:16
    2. Application Scalars Vectors and Matrices 00:18:42
    3. LAB 1 Intro Scalars Vectors and Matrices 00:01:39
    4. LAB 1 Solution Scalars Vectors and Matrices 00:12:15
    5. Vector Operations 00:12:00
    6. Application Vector Operations 00:22:10
    7. LAB 2 Intro Vector Operations 00:01:54
    8. LAB 2 Solution Vector Operations 00:11:55
    9. Matrix Operations Addition Subtraction Multiplication 00:17:31
    10. Application Matrix Operations Addition Subtraction Multiplication 00:11:08
    11. LAB 3 Intro Matrix Operations Addition Subtraction Multiplication 00:01:12
    12. LAB 3 Solution Matrix Operations Addition Subtraction Multiplication 00:04:07
    13. Matrix Operations Transposes and Inverses 00:11:33
    14. Application Matrix Operations Transposes and Inverses 00:12:54
    15. LAB 4 Intro Matrix Operations Transposes and Inverses 00:01:01
    16. LAB 4 Solution Matrix Operations Transposes and Inverses 00:03:20
    17. What is Linear Regression 00:11:27
    18. Application What is Linear Regression 00:28:05
    19. LAB 5 Intro What is Linear Regression 00:02:17
    20. Lab 5 Solution What is Linear Regression 00:12:12
    21. Matrix Representation of Linear Regression 00:12:28
    22. Application Matrix Representation of Linear Regression 00:13:37
    23. Lab 6 Intro Matrix Representation of Linear Regression 00:03:22
    24. Lab 6 Solution Matrix Representation of Linear Regression 00:12:45
  4. Chapter 4 : Section Calculus
    1. Functions and Tangent Lines 00:15:29
    2. Application Functions and Tangent Lines 00:18:29
    3. Lab 1 Intro Functions and Tangent Lines 00:01:48
    4. Lab 1 Solution Functions and Tangent Lines 00:13:11
    5. Derivatives 00:09:48
    6. Application Derivatives 00:18:35
    7. Lab 2 Intro Derivatives 00:02:36
    8. Lab 2 Solution Derivatives 00:14:58
    9. Optimization Using Derivatives Single Variable Functions 00:11:58
    10. Application Optimization Using Derivatives Single Variable 00:10:23
    11. Intro Optimization Using Derivatives Single Variable Function 00:01:27
    12. Lab 3 Solution Optimization Using Derivatives Single Variable Function 00:08:15
    13. Optimization Using Derivatives Two Variable Functions 00:10:42
    14. Application Optimization Using Derivatives Two Variable Function 00:17:03
    15. Lab 4 Intro Optimization Using Derivatives Two Variable Functions 00:02:25
    16. Lab 4 Solution Optimization Using Derivatives Two Variable Function 00:05:03
    17. Linear Regression the Calculus Optimization Perspective 00:20:00
    18. Application Linear Regression the Calculus Optimization Perspective 00:16:42
    19. Lab 5 Intro Linear Regression the Calculus Optimization Perspective 00:02:57
    20. Lab 5 Solution Linear Regression the Calculus Optimization Perspective 00:14:27
  5. Chapter 5 : Tying it All Together Vector Calculus
    1. Orthogonal Vectors and Linear Independence 00:10:32
    2. Application Orthogonal Vectors and Linear Independence 00:13:15
    3. Lab 1 Intro Orthogonal Vectors and Linear Independence 00:02:47
    4. Lab 1 Solution Orthogonal Vectors and Linear Independence 00:12:07
    5. Eigenvectors and Eigenvalues 00:12:47
    6. Application Eigenvectors and Eigenvalues 00:09:51
    7. Lab 2 Intro Eigenvectors and Eigenvalues 00:00:50
    8. Lab 2 Solution Eigenvectors and Eigenvalues 00:04:42
    9. Vectors Gradient Descent 00:10:03
    10. Application Vectors Gradient Descent 00:10:51
    11. Lab 3 Intro Vectors Gradient Descent 00:01:21
    12. Lab 3 Solution Vectors Gradient Descent 00:12:50
    13. Linear Regression the Gradient Descent Perspective 00:04:17
    14. Application Linear Regression the Gradient Descent Perspective 00:17:56
    15. Lab 4 Intro Linear Regression the Gradient Descent Perspective 00:01:16
    16. Lab 4 Solution Linear Regression the Gradient Descent Perspective 00:07:20

Product information

  • Title: Mathematics for Data Science and Machine Learning using R
  • Author(s): Eduonix
  • Release date: September 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781839210945