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.

❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp

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

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