Practice linear algebra for machine learning readiness.
This Gradus course covers linear algebra and matrix methods through structured lessons, concept dependencies, deterministic feedback, and review practice.
Course topics
- Vectors, matrices, linear combinations, and matrix products
- Subspaces, rank, bases, dimension, and the four fundamental spaces
- Orthogonality, projections, Gram-Schmidt, least squares, and regression
- Eigenvalues, spectral geometry, positive definite matrices, and SVD
- Graph matrices, Markov matrices, Fourier methods, and data-matrix applications
Published package
The remote course ID is linear_algebra_strang. It includes 16 chapters, 78 lessons, and 269 concepts.