Python for AI & ML Engineers

Variables & Types

Python's core data types and dynamic typing.

Lists, Dicts, Sets, Tuples

The data structures you'll use every day.

Control Flow

if, for, while, and Python's iteration helpers.

Functions & Lambdas

Arguments, defaults, *args/**kwargs, and closures.

Comprehensions & Generators

Concise transforms and memory-efficient streaming.

Classes & Dataclasses

Object-oriented patterns used in every ML framework.

Decorators & Context Managers

Wrap behavior: timing, caching, resource cleanup.

NumPy Essentials

Vectorized arrays — the foundation of ML in Python.

Pandas for Data Wrangling

Load, clean, and aggregate tabular data.

scikit-learn Workflow

Split, fit, evaluate — the classic ML loop.

PyTorch Training Loop

Tensors, autograd, and the canonical training loop.

Calling LLM APIs

HTTP requests, async, and structured outputs.

Typed Data with Pydantic

Validate LLM outputs and API payloads.

Errors, Logging & Testing

Production habits that keep pipelines reliable.