Mathematics for Data Science and Machine Learning using R

Learn the fundamental mathematics for Data Science, AI &ML using R

Created by Eduonix Learning Solutions, Last Updated 05-Oct-2019, Language:English

What Will I Get ?

Master the fundamental mathematical concepts required for Datas Science and Machine Learning
Learn to implement mathematical concepts using R
Master Linear alzebra, Calculus and Vector calculus from ground up
Master R programming langauge

Requirements

Basic knowledge of Statistics and Mathematics is required to complete the course

Description

Without a doubt, data science has become one of the most important fields in the current world. From banking to healthcare to business, it is essential for everyone and that is the main reason behind the significant increase in the demand of data science expert throughout the world. Considering all these things, we have designed this online course which teaches you all mathematics of data science including R programming language. R programming language was created especially for data analysis, performing statistics and graphical representation in a better way.

What makes this online tutorial unique?

Though several tutorials on data science are already available, this online course is one of its kind guide having hand-picked topics revolving around data science. It includes some of the essential concepts of foundational mathematics for data science using R programming language. You will explore numerous sections with over 9 hours of video content lectured by one of the world’s veteran statistician and data scientists, Timothy Young. He explains all the concepts in the most simplest way possible for easy and effective learning of mathematics behind data science.

This course includes -

Introduction- Data Science, Machine Learning & R Programming Language
Linear Algebra- Scalars, Vectors & Metrices
Section Calculus- Function & tangent Lines, Derivatives
Vector Calculus -
- Orthogonal Vectors and Linear Independence
- Eigenvectors and Eigenvalues
- Vectors Gradient Descent, and so much more!

It is clear that data science is becoming important for both developers as well as industries causing a significant increase in the demand of the experts. Like several others, if you are the one who is facing difficulties with all the mathematical concepts for data science then this is the right time and this online tutorial is the right course designed for exclusively for you!

Course Content

Introduction 1 Lectures 00:01:01
- Introduction
  
  Preview
  
  00:01:01
Overview of R 3 Lectures 00:36:27
- Overview of R Workspace & Basic Commands
  
  Preview
  
  00:22:50
- LAB 1 Introduction
  
  Preview
  
  00:02:27
- LAB 1 Solution
  
  00:11:10
Linear Algebra 24 Lectures 04:11:52
- Scalars Vectors and Matrices
  
  00:12:15
- Application Scalars Vectors and Matrices
  
  00:18:41
- LAB 1 Intro Scalars Vectors and Matrices
  
  00:01:38
- LAB 1 Solution Scalars Vectors and Matrices
  
  00:12:15
- Vector Operations
  
  00:11:59
- Application Vector Operations
  
  00:22:10
- LAB 2 Intro Vector Operations
  
  00:01:54
- LAB 2 Solution Vector Operations
  
  00:11:55
- Matrix Operations Addition Subtraction Multiplication
  
  00:17:40
- Application Matrix Operations Addition Subtraction Multiplication
  
  00:11:08
- LAB 3 Intro Matrix Operations Addition Subtraction Multiplication
  
  00:01:12
- LAB 3 Solution Matrix Operations Addition Subtraction Multiplication
  
  00:04:07
- Matrix Operations Transposes and Inverses
  
  00:11:33
- Application Matrix Operations Transposes and Inverses
  
  00:12:54
- LAB 4 Intro Matrix Operations Transposes and Inverses
  
  00:01:00
- LAB 4 Solution Matrix Operations Transposes and Inverses
  
  00:03:19
- What is Linear Regression
  
  00:11:27
- Application What is Linear Regression
  
  00:28:05
- LAB 5 Intro What is Linear Regression
  
  00:02:17
- Lab 5 Solution What is Linear Regression
  
  00:12:12
- Matrix Representation of Linear Regression
  
  00:12:28
- Application Matrix Representation of Linear Regression
  
  00:13:37
- Lab 6 Intro Matrix Representation of Linear Regression
  
  00:03:21
- Lab 6 Solution Matrix Representation of Linear Regression
  
  00:12:45
Section Calculus 20 Lectures 03:36:21
- Functions and Tangent Lines
  
  00:15:31
- Application Functions and Tangent Lines
  
  00:18:31
- Lab 1 Intro Functions and Tangent Lines
  
  00:01:51
- Lab 1 Solution Functions and Tangent Lines
  
  00:13:12
- Derivatives
  
  00:09:50
- Application Derivatives
  
  00:18:35
- Lab 2 Intro Derivatives
  
  00:02:38
- Lab 2 Solution Derivatives
  
  00:14:58
- Optimization Using Derivatives Single Variable Functions
  
  00:11:58
- Application Optimization Using Derivatives Single Variable
  
  00:10:22
- Intro Optimization Using Derivatives Single Variable Function
  
  00:01:26
- Lab 3 Solution Optimization Using Derivatives Single Variable Function
  
  00:08:15
- Optimization Using Derivatives Two Variable Functions
  
  00:10:42
- Application Optimization Using Derivatives Two Variable
  
  00:17:03
- Lab 4 Intro Optimization Using Derivatives Two Variable Functions
  
  00:02:25
- Lab 4 Solution Optimization Using Derivatives Two Variable Function
  
  00:05:02
- Linear Regression The Calculus Optimization Perspective
  
  00:19:59
- Application Linear Regression The Calculus Optimization Perspective
  
  00:16:41
- Lab 5 Intro Linear Regression The Calculus Optimization Perspective
  
  00:02:56
- Lab 5 Solution Linear Regression The Calculus Optimization Perspective
  
  00:14:26
Tying it All Together Vector Calculus 16 Lectures 02:12:40
- Orthogonal Vectors and Linear Independence
  
  00:10:32
- Application Orthogonal Vectors and Linear Independence
  
  00:13:15
- Lab 1 Intro Orthogonal Vectors and Linear Independence
  
  00:02:47
- Lab 1 Solution Orthogonal Vectors and Linear Independence
  
  00:12:07
- Eigenvectors and Eigenvalues
  
  00:12:47
- Application Eigenvectors and Eigenvalues
  
  00:09:50
- Lab 2 Intro Eigenvectors and Eigenvalues
  
  00:00:49
- Lab 2 Solution Eigenvectors and Eigenvalues
  
  00:04:42
- Vectors Gradient Descent
  
  00:10:02
- Application Vectors Gradient Descent
  
  00:10:51
- Lab 3 Intro Vectors Gradient Descent
  
  00:01:21
- Lab 3 Solution Vectors Gradient Descent
  
  00:12:50
- Linear Regression The Gradient Descent Perspective
  
  00:04:17
- Application Linear Regression The Gradient Descent Perspective
  
  00:17:55
- Lab 4 Intro Linear Regression The Gradient Descent Perspective
  
  00:01:15
- Lab 4 Solution Linear Regression The Gradient Descent Perspective
  
  00:07:20

Eduonix Learning Solutions

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