English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 11h 57m | 3.19 GB
Learn the basic math for Data Science, AI, and ML using R
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.
Learn
- Master the basic math concepts you need for data science and Machine Learning
- Learn to implement mathematical concepts using R
- Master linear algebra, calculus, and vector calculus from the ground up
- Master the R programming language
Table of Contents
Introduction
1 Intro
Overview of R
2 Introduction
3 Overview of R Workspace & Basic Commands
4 LAB 1 Intro
5 LAB 1 Solution
Linear Algebra
6 Scalars Vectors and Matrices
7 Application Scalars Vectors and Matrices
8 LAB 1 Intro Scalars Vectors and Matrices
9 LAB 1 Solution Scalars Vectors and Matrices
10 Vector Operations
11 Application Vector Operations
12 LAB 2 Intro Vector Operations
13 LAB 2 Solution Vector Operations
14 Matrix Operations Addition Subtraction Multiplication
15 Application Matrix Operations Addition Subtraction Multiplication
16 LAB 3 Intro Matrix Operations Addition Subtraction Multiplication
17 LAB 3 Solution Matrix Operations Addition Subtraction Multiplication
18 Matrix Operations Transposes and Inverses
19 Application Matrix Operations Transposes and Inverses
20 LAB 4 Intro Matrix Operations Transposes and Inverses
21 LAB 4 Solution Matrix Operations Transposes and Inverses
22 What is Linear Regression
23 Application What is Linear Regression
24 LAB 5 Intro What is Linear Regression
25 Lab 5 Solution What is Linear Regression
26 Matrix Representation of Linear Regression
27 Application Matrix Representation of Linear Regression
28 Lab 6 Intro Matrix Representation of Linear Regression
29 Lab 6 Solution Matrix Representation of Linear Regression
Section Calculus
30 Functions and Tangent Lines
31 Application Functions and Tangent Lines
32 Lab 1 Intro Functions and Tangent Lines
33 Lab 1 Solution Functions and Tangent Lines
34 Derivatives
35 Application Derivatives
36 Lab 2 Intro Derivatives
37 Lab 2 Solution Derivatives
38 Optimization Using Derivatives Single Variable Functions
39 Application Optimization Using Derivatives Single Variable
40 Intro Optimization Using Derivatives Single Variable Function
41 Lab 3 Solution Optimization Using Derivatives Single Variable Function
42 Optimization Using Derivatives Two Variable Functions
43 Application Optimization Using Derivatives Two Variable Function
44 Lab 4 Intro Optimization Using Derivatives Two Variable Functions
45 Lab 4 Solution Optimization Using Derivatives Two Variable Function
46 Linear Regression the Calculus Optimization Perspective
47 Application Linear Regression the Calculus Optimization Perspective
48 Lab 5 Intro Linear Regression the Calculus Optimization Perspective
49 Lab 5 Solution Linear Regression the Calculus Optimization Perspective
Tying it All Together Vector Calculus
50 Orthogonal Vectors and Linear Independence
51 Application Orthogonal Vectors and Linear Independence
52 Lab 1 Intro Orthogonal Vectors and Linear Independence
53 Lab 1 Solution Orthogonal Vectors and Linear Independence
54 Eigenvectors and Eigenvalues
55 Application Eigenvectors and Eigenvalues
56 Lab 2 Intro Eigenvectors and Eigenvalues
57 Lab 2 Solution Eigenvectors and Eigenvalues
58 Vectors Gradient Descent
59 Application Vectors Gradient Descent
60 Lab 3 Intro Vectors Gradient Descent
61 Lab 3 Solution Vectors Gradient Descent
62 Linear Regression the Gradient Descent Perspective
63 Application Linear Regression the Gradient Descent Perspective
64 Lab 4 Intro Linear Regression the Gradient Descent Perspective
65 Lab 4 Solution Linear Regression the Gradient Descent Perspective
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