Linear Algebra for ML

Github repository: https://github.com/w2sg-arnav/Linear-...
X: https://x.com/w2sgarnav

A comprehensive, from-the-ground-up series on Linear Algebra specifically designed for Machine Learning practitioners. This series combines theoretical foundations with practical implementations and real-world ML applications, incorporating insights from leading courses and resources worldwide.
By the end of this series, you will:
Master the fundamentals of Linear Algebra from vectors to matrix decompositions
Understand how Linear Algebra concepts are used in ML algorithms
Implement key algorithms from scratch using NumPy
Apply concepts to real ML problems like PCA, SVD, and Neural Networks
Build intuition through interactive visualizations and examples
Inspired by resources from:

Gilbert Strang's MIT Linear Algebra
3Blue1Brown's Essence of Linear Algebra
Imperial College London's Mathematics for ML
DeepLearning.AI's Linear Algebra course
fast.ai's Computational Linear Algebra

Visual-First Learning: Every concept is introduced through intuitive visualizations inspired by 3Blue1Brown
Code-Driven Understanding: Implementations in Python from scratch, then using optimized libraries
ML-Focused Examples: Real applications in neural networks, computer vision, NLP, and more
Comprehensive Coverage: From basic vectors to advanced topics like manifold learning
Interactive Learning: Hands-on exercises, coding challenges, and real-world projects