Linear Algebra for Machine Learning
By freeCodeCamp.org
Published: Feb 27, 2025“
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:
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