Python & Math for AI Notes
Subject Overview
The opening semester of Year 1 (Foundations), aimed at becoming productive in Python and comfortable with the mathematical language of machine learning. Five modules of 4 hours each take you from variables and control flow through the scientific Python stack to the linear algebra, calculus and probability that the rest of the programme assumes. Year 1 as a whole builds the technical bedrock: programming fluency, the maths you actually need, and the classical ML techniques that still power most real-world systems.
Unit-wise Syllabus
7 units — click WhatsApp below to get the full notes for each
Unit 1: Python Essentials (4 hours)
Variables, control flow, functions, data structures, file I/O, exception handling, building muscle memory in the editor
Unit 2: Pythonic Code & OOP (4 hours)
List comprehensions, generators, decorators, classes, modules, virtual environments (venv, uv), packaging basics
Unit 3: Scientific Python Stack (4 hours)
NumPy arrays and broadcasting, Pandas DataFrames, Matplotlib and Seaborn for visualization
Unit 4: Linear Algebra for ML (4 hours)
Vectors, matrices, dot products, matrix multiplication, eigenvalues, the intuition behind embeddings
Unit 5: Calculus & Probability (4 hours)
Derivatives, gradients, the chain rule, probability distributions, Bayes' theorem, expectation
Unit 6: Tools introduced
VS Code or Cursor as primary editor, Git and GitHub (branches, PRs, basic collaboration), Jupyter notebooks and Google Colab, GitHub Copilot for inline suggestions — learning when to accept versus reject
Unit 7: Mini-projects
CLI expense tracker with file persistence, stock price analyzer using NumPy/Pandas on a real CSV, a tiny linear regression solver built from scratch with no libraries