Checkpoint: Projections, Eigen and SVD
This lesson includes a 10-question quiz.
About this course
Part of Mathematics for Machine Learning 1: Linear Algebra Foundations — Build the linear algebra intuition behind machine learning: see vectors and matrices as transformations, then compute eigenvalues, projections and SVD yourself in NumPy.
- Level
- Beginner
- Goal
- Fundamentals
- Language
- English
- Focus
- vectors and norms matrix operations and transformations eigenvalues and SVD geometric intuition NumPy practice
- Prior knowledge
- High-school algebra (equations, functions), Basic Python or willingness to run short NumPy snippets
- Practice
- Guided exercises
- Time
- ~4.8h
- Voice
- a patient university lecturer who draws every operation as a geometric transformation before touching the algebra
- Not covered
- Calculus and derivatives, Probability and statistics, Deep learning models
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