Math

Foundational mathematics for machine learning — linear algebra operations progressing from manual Python implementations to vectorized NumPy.

Modules

Learning Path

1. Python Slicing (Tasks 0–1): Master array indexing and 2D extraction
2. Manual Matrix Operations (Tasks 2–8): Build intuition with nested loops — shape, transpose, add, concat, multiply
3. NumPy Transition (Task 9): First contact with NumPy arrays and transpose
4. NumPy Vectorization (Tasks 10–14): Replace loops with built-in methods — shape, transpose, broadcasting, @ operator
5. Advanced Generalization (Tasks 100–102): N-dimensional operations with recursion, slice objects, tuple indexing

Resources

• NumPy Documentation
• Python Official Docs