Linear Algebra for Machine Learning
This in-depth course provides a comprehensive exploration of all critical linear algebra concepts necessary for machine learning. You will learn the mathematical foundations to excel in AI.
Course created by @LunarTech_ai.
More AI courses here: https://lunartech.ai/
❤️ Try interactive Python courses we love, right in your browser: https://scrimba.com/freeCodeCamp-Python (Made possible by a grant from our friends at Scrimba)
Timestamps
00:00:00 - Introduction
00:02:09 - Essential Trigonometry and Geometry Concepts
00:10:52 - Real Numbers and Vector Spaces
00:15:05 - Norms, Refreshment fro…
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Chapters (33)
Introduction
2:09
Essential Trigonometry and Geometry Concepts
10:52
Real Numbers and Vector Spaces
15:05
Norms, Refreshment from Trigonometry
19:52
The Cartesian Coordinates System
24:37
Angles and Their Measurement
38:00
Norm of a Vector
44:08
The Pythagorean Theorem
52:00
Norm of a Vector
56:00
Euclidean Distance Between Two Points
1:11:33
Foundations of Vectors
1:12:50
Scalars and Vectors, Definitions
1:42:28
Zero Vectors and Unit Vectors
1:49:39
Sparsity in Vectors
1:52:39
Vectors in High Dimensions
1:55:14
Applications of Vectors, Word Count Vectors
2:03:22
Applications of Vectors, Representing Customer Purchases
2:39:22
Advanced Vectors Concepts and Operations
2:40:40
Scalar Multiplication Definition and Examples
3:04:27
Linear Combinations and Unit Vectors
3:51:37
Span of Vectors
4:31:42
Linear Independence
5:03:34
Linear Systems and Matrices, Coefficient Labeling
5:20:24
Matrices, Definitions, Notations
5:50:24
Special Types of Matrices, Zero Matrix
6:25:25
Algebraic Laws for Matrices
7:21:56
Determinant Definition and Operations
8:12:47
Vector Spaces, Projections
8:20:05
Vector Spaces Example, Practical Application
9:14:33
Vector Projection Example
9:29:35
Understanding Orthogonality and Normalization
10:06:29
Special Matrices and Their Properties
10:21:07
Orthogonal Matrix Examples
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