A self-contained two-day unit to build the matrix foundation that later units rely on heavily — SVD, PCA, image compression, and neural network weight updates all speak this language. The goal is fluency with NumPy matrix operations, not proof-writing.

Concepts You’ll Learn About

  • Vectors and matrices — notation, shapes, the difference between a row and a column vector
  • Matrix multiplication — how dot products chain together; why order matters
  • Transpose and inverse — what they do geometrically and when they exist
  • NumPy array operations — indexing, slicing, broadcasting, @ for matrix multiply

Topics

  • Linear algebra in Python — vectors, matrices, and the NumPy operations that manipulate them.

  • Matrix indexing warmup — practice selecting rows, columns, and submatrices; builds the indexing fluency used everywhere else.

  • Intro to matrices in NumPy — matrix multiply, transpose, inverse, and solving linear systems.

What’s next

Unit 04 is the first end-to-end ML project — you’ll use everything from Units 1–3 to clean, explore, and model a real dataset.