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freeCodeCamp

Linear Algebra for Machine Learning and Generative AI

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Overview

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This course introduces linear algebra for machine learning and generative AI, covering vectors, vector spaces, norms, dot products, matrices, and linear systems. It includes worked examples using Gaussian elimination and reduced row echelon form.

Syllabus

⌨️ Introduction to the course
⌨️ Linear Algebra Roadmap for 2024
⌨️ Course Prerequisites
⌨️ Refreshment: Real Numbers and Vector Spaces
⌨️ Refreshment: Norms and Euclidean Distance
⌨️ Why These Prerequisites Matter
⌨️ Foundations of Vectors
⌨️ Vector - Geometric Representation Example
⌨️ Special Vectors
⌨️ Application of Vectors
⌨️ Vectors Operations and Properties
⌨️ Advanced Vectors and Concepts
⌨️ Length of a Vector - def and example
⌨️ Length of Vector - Geometric Intuition
⌨️ Dot Product
⌨️ Dot Product, Length of Vector and Cosine Rule
⌨️ Cauchy Schwarz Inequality - Derivation & Proof
⌨️ Introduction to Linear Systems
⌨️ Introduction to Matrices
⌨️ Core Matrix Operations
⌨️ Solving Linear Systems - Gaussian Elimination
⌨️ Detailed Example - Solving Linear Systems
⌨️ Detailed Example - Reduced Row Echelon Form Augmented Matrix,REF, RREF

Taught by

freeCodeCamp.org

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