Python Engineering for AI — Roadmap

1. Language Fundamentals: OOP, Functional & Modern Python

• OOP: classes, inheritance, composition, magic methods
• Functional programming in Python: map/filter/reduce, pure functions, immutability
• Iterators and generators, yield, lazy evaluation
• Decorators: how they work, writing your own
• Context managers: with, __enter__/__exit__, contextlib
• Type hints, typing, static type checking with mypy
• dataclasses

2. Concurrency, Memory & Performance

• async/await, the event loop, when async actually helps
• multiprocessing vs. threading in practice (see the GIL in CS Fundamentals)
• Reference counting and the cyclic garbage collector
• Profiling Python code: cProfile, py-spy, memory_profiler
• Performance optimization: vectorization over loops, avoiding unnecessary copies

3. Packaging, Testing & Tooling

• pyproject.toml, dependency pinning, lockfiles
• Virtual environments: venv, uv, why isolation matters
• pytest: fixtures, parametrization, marks, conftest.py
• Logging: the logging module vs. print
• Debugging: pdb, breakpoints, post-mortem debugging

Next: Mathematics for AI — the math this engineering foundation runs.