Fundamentals to Linear Algebra

20h 53m 19s
English
Paid

Course description

Unleash the power of linear algebra to conquer the world of data science, machine learning, and artificial intelligence. This intensive course will transform you into a master of mathematics, ready to tackle real-world challenges with practical skills and unwavering confidence.

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#1: Welcome Message

All Course Lessons (35)

#Lesson TitleDurationAccess
1
Welcome Message Demo
14:18
2
Linear Algebra RoadMap 2024
19:12
3
Pre-Requisites Introduction
09:03
4
Refreshment - Norms & Euclidean Distance
10:31
5
Refreshment - Real Numbers and Vector Space
04:14
6
Refreshment - Cartesian Coordinate System & Unit Circle
04:45
7
Refreshment - Angles, Unit Circle and Trigonometry
13:24
8
Refreshment - Pythagorean Theorem & Orthogonality
05:25
9
Why these Pre-Requisites Matter
02:28
10
Module 2.1: Foundations of Vectors
30:55
11
Module 2.2: Special Vectors and Operations
56:53
12
Module 2.3: Part 1 - Scalar Multiplication
25:06
13
Module 2.3 Part 2 - Linear Combination and Unit Vectors
47:10
14
Module 2.3 Part 3 - Span of Vectors
40:06
15
Module 2.3: Part 4 - Linear Independence
31:53
16
Module 2.4: Dot Product, Cauchy-Schwarz Inequality and Its
01:29:45
17
Module 1: Foundations of Linear Systems and Matrices
16:50
18
Module 2: Introduction to Matrices
30:01
19
Module 3: Core Matrix Operations
35:01
20
Module 4: Part 1 Solving Linear Systems - Gaussian Reduction
47:23
21
Module 4: Part 2 Solving Linear Systems - Gaussian Reduction
01:07:47
22
Module 4: Part 3 Solving Linear Systems - Gaussian Reduction
01:10:00
23
Module 4: Part 4 Solving Linear Systems - Gaussian Reduction
53:47
24
Module 1: Algebraic Laws for Matrices
56:31
25
Module 2: Determinants and Their Properties
50:52
26
Module 3: Matrix Inverses and Identity Matrix
01:03:32
27
Module 4: Transpose of Matrices: Properties and Applications
23:15
28
Module 1: Part 1 Basis of Vector Space
35:39
29
Module 1: Part 2 Vector Projection and Calculation
41:12
30
Module 1: Part 3 Gram-Schmidt Process
36:53
31
Module 2: Special Matrices and Their Properties
14:39
32
Module 3: Matrix Factorization, Examples and Applications
27:03
33
Module 4: QR Decomposition Overview
42:52
34
Module 5: Eigenvalues, Eigenvectors, and Eigen Decomposition
01:16:31
35
Module 6: Singular Value Decomposition (SVD)
58:23

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