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Video course · 15 chapters · 649 min · certificate

Linear Algebra for Machine Learning

A comprehensive linear algebra course for machine learning: trigonometry and norms, Euclidean distance, vectors and their operations, linear combinations, span, linear independence, matrices and linear systems, special matrices, algebraic laws, determinants, vector spaces, projections, orthogonality and orthogonal matrices.

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What you'll learn

  • Compute norms and distances
  • Work with vectors and linear combinations
  • Determine span and linear independence
  • Solve linear systems with matrices
  • Compute determinants
  • Apply projections and orthogonality

Chapters

15 chapters · 648:43
  1. 56:00 01Foundations Members Foundations: trig, reals and norms Prerequisites.
  2. 46:28 02Vectors Members Euclidean distance and vectors Scalars vs vectors.
  3. 56:54 03Applications Members Special vectors and applications Zero, unit, sparse.
  4. 25:05 04Operations Members Vector operations and scalar multiplication Add, subtract, scale.
  5. 47:10 05Combinations Members Linear combinations Scaled sums.
  6. 40:05 06Span Members Span All combinations.
  7. 31:52 07Independence Members Linear independence No redundancy.
  8. 46:50 08Matrices Members Linear systems and matrices Notation.
  9. 35:01 09Special Members Special matrices: zero matrix Types.
  10. 56:31 10Laws Members Algebraic laws for matrices Solving systems.
  11. 50:51 11Determinant Members Determinants Definition and operations.
  12. 7:18 12Spaces Members Vector spaces and projections Areas and volumes.
  13. 54:28 13Basis Members Vector spaces: practical example Column and null space.
  14. 51:56 14Orthogonality Members Projections, orthogonality, normalisation Project a onto b.
  15. 42:14 15Orthogonal Members Special and orthogonal matrices Properties.

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