Learning Python

The tutorials linked from this section are primarily ML-focused — they use Python to explore ideas in machine learning and data science, and can be approached in any order depending on what interests you most.

They don't assume much Python knowledge to start with, but they do assume you're comfortable with the ideas in How to Learn Programming. If you've never written code before, that's the right place to start.

Why Python for Machine Learning?

Python has become the dominant language for ML and data science work for a few reasons: its syntax is relatively readable, the library ecosystem (NumPy, pandas, scikit-learn, PyTorch) is mature, and Jupyter notebooks make it easy to explore data interactively alongside explanatory text. These tutorials use that environment.

Getting Started

The tutorials here can be opened directly in Google Colab — no installation required. If you'd prefer to run things locally, Anaconda is the easiest way to get a working Python environment with the common scientific libraries included.

A note on approach: the tutorials are not a comprehensive Python course. They're designed to give you enough Python to do something interesting with data, and let curiosity guide the rest.