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.

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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 from Trigonometry
00:19:52 - The Cartesian Coordinates System
00:24:37 - Angles and Their Measurement
00:38:00 - Norm of a Vector
00:44:08 - The Pythagorean Theorem
00:52:00 - Norm of a Vector
00:56:00 - Euclidean Distance Between Two Points
01:11:33 - Foundations of Vectors
01:12:50 - Scalars and Vectors, Definitions
01:42:28 - Zero Vectors and Unit Vectors
01:49:39 - Sparsity in Vectors
01:52:39 - Vectors in High Dimensions
01:55:14 - Applications of Vectors, Word Count Vectors
02:03:22 - Applications of Vectors, Representing Customer Purchases
02:39:22 - Advanced Vectors Concepts and Operations
02:40:40 - Scalar Multiplication Definition and Examples
03:04:27 - Linear Combinations and Unit Vectors
03:51:37 - Span of Vectors
04:31:42 - Linear Independence
05:03:34 - Linear Systems and Matrices, Coefficient Labeling
05:20:24 - Matrices, Definitions, Notations
05:50:24 - Special Types of Matrices, Zero Matrix
06:25:25 - Algebraic Laws for Matrices
07:21:56 - Determinant Definition and Operations
08:12:47 - Vector Spaces, Projections
08:20:05 - Vector Spaces Example, Practical Application
09:14:33 - Vector Projection Example
09:29:35 - Understanding Orthogonality and Normalization
10:06:29 - Special Matrices and Their Properties
10:21:07 - Orthogonal Matrix Examples

🎉 Thanks to our Champion and Sponsor supporters:
👾 Drake Milly
👾 Ulises Moralez
👾 Goddard Tan
👾 David MG
👾 Matthew Springman
👾 Claudio
👾 Oscar R.
👾 jedi-or-sith
👾 Nattira Maneerat
👾 Justin Hual

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