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Linear algebra for machine learning

Vectors, matrices, decompositions and what they mean geometrically — the parts machine learning actually uses. Placeholder content.

10 lessons · about 3.3 hours · Knows basic algebra, wants the linear algebra machine learning assumes

The syllabus, mapped

  1. 01Vectors and linear combinations — Everything is a vector.
  2. 02Matrices as transformations — The geometric reading that makes the rest click.
  3. 03Systems and rank — When a solution exists, and how many.
  4. 04Eigenvalues and eigenvectors — PCA and much else stand on them.
  5. 05Singular value decomposition — The decomposition ML leans on most.

The 10 lessons

  1. 01Vectors and linear combinations: the ideas · free
  2. 02Vectors and linear combinations: exam problems
  3. 03Matrices as transformations: the ideas
  4. 04Matrices as transformations: exam problems
  5. 05Systems and rank: the ideas
  6. 06Systems and rank: exam problems
  7. 07Eigenvalues and eigenvectors: the ideas
  8. 08Eigenvalues and eigenvectors: exam problems
  9. 09Singular value decomposition: the ideas
  10. 10Singular value decomposition: exam problems

The first lesson is free. The other 9 (3.3 hours in all) cost $8.42 bought together.

Or $1.70 a lesson one at a time, or $20.00 a month for Minimal — cheaper once you take more than ~11 lessons a month.

Try a block from the first lesson: Vectors and linear combinations: the ideas
Linear algebra · transformsDET 1.00
matrix[1.00 0.00 ; 0.00 1.00]det1.00

The square spans 1.00 units of area. That is the determinant, and it is the factor every area in the plane is multiplied by — drag a vector until the square is twice the size and the whole plane doubles with it.

Solve 3x−7=113x - 7 = 11.

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