The math behind machine learning — from vectors to transformers
Introduction to linear algebra for machine learning with Python and NumPy fundamentals
Understanding vectors, vector operations, and geometric interpretations in machine learning
Master matrices, transformations, and fundamental matrix decompositions
Solve systems of linear equations and fit models to data using least squares
Understand eigendecomposition, spectral properties, and their applications
Master SVD and its powerful applications in machine learning and data analysis
Principal Component Analysis and advanced dimensionality reduction techniques
Applications of linear algebra in deep learning and neural network architectures