Linear Algebra for AI
Master the language of AI: vectors, matrices, transformations, and eigenvalues—with Python code to ground every concept.
8 modules0 available~10 hours total
About This Course
Linear algebra is the mathematical backbone of modern AI and machine learning. This course teaches you to think in vectors and matrices, with every concept grounded in executable Python code.
You won't just memorize formulas—you'll build intuition for what linear transformations actually do, visualize high-dimensional spaces, and understand why eigenvalues matter for everything from Google's PageRank to neural networks.
Inspired by Gilbert Strang's MIT 18.06 and 3Blue1Brown's Essence of Linear Algebra.
Prerequisites
- Basic algebra (solving equations, working with variables)
- Familiarity with Python basics helpful but not required
What You Will Learn
- Understand vectors, matrices, and their geometric interpretations
- Perform matrix operations and understand their computational meaning
- Solve systems of linear equations using Gaussian elimination
- Find eigenvalues and eigenvectors and understand their applications
- Apply linear algebra concepts using NumPy
Your Learning Path
Each module builds on the last. Take your time—the AI tutor is with you at every step.
1
Vectors and Vector Spaces — What are vectors? From arrows to abstract spaces
45 minComing soon
2
Matrix Operations — Addition, multiplication, and the geometry of transformations
45 minComing soon
3
Systems of Linear Equations — Gaussian elimination and row reduction
45 minComing soon
4
Vector Spaces and Subspaces — Linear independence, span, and basis
45 minComing soon
5
Linear Transformations — Functions that preserve vector structure
45 minComing soon
6
Determinants — The scaling factor of transformations
45 minComing soon
7
Eigenvalues and Eigenvectors — The directions that don't rotate
50 minComing soon
8
Applications to AI/ML — PCA, SVD, and why this matters for machine learning
50 minComing soon