Python for AI

Every course and workshop in this material assumes you can write Python well enough to turn a notebook into a project. That is exactly the gap Python for AI Engineering closes — not with exercises, but by growing one real project, a podcast aggregator, from a Jupyter notebook into a structured command-line application.

The arc of the course

• Fundamentals: variables, data structures, loops, f-strings, control flow.
• More fundamentals applied immediately: keyword search, description previews, category filters, refactoring — each feature pushes the project forward.
• Code organization: from notebook to script, extracting data, display, and filters modules, guarding the entry point, running from the command line.
• External data: uv and packages, requests and HTTP, files, JSON and CSV.
• Data processing with pandas.
• Robustness: errors and testing.
• Object-oriented and intermediate Python.

Why a project instead of exercises

The course states the design directly: you learn Python by writing it, on a project that keeps growing. By the end, the same project has forced you through structure, external data, error handling, and tests — the exact movements the AI Engineering Buildcamp homework expects.

Where this fits

The AI Engineering Roadmap puts this course at stage 0, before AI Hero and the buildcamp. If you already write Python fluently, skip it — the roadmap assumes its outcomes, not its completion certificate.

Where to go next

• The rest of the route: AI Engineering Roadmap
• The first thing to build with it: AI Hero: 7-Day AI Agents Crash-Course