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Linear Algebra for Machine Learning
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A video course on linear algebra for machine learning, starting with trigonometry and geometry refreshers and building up to vectors, matrices and determinants.
The chapter list includes applications such as word count vectors and representing customer purchases, which show why sparse, high-dimensional vectors appear in data work.
The video is labelled beginner and suits readers who have not used linear algebra before. Chapter timestamps make it easy to skip the parts you already know.
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/
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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
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