Every track is a sequence of the same free courses — ordered so each one gives you what the next assumes you already know. Pick the role you're aiming at and start at the top.
Build and ship models and LLM applications.
Start with Python fundamentals, get fluent in the numerical stack, then move into classical ML before layering on generative AI and agent frameworks.
Design and run the services behind a product.
Python and SQL first, then REST design, a production framework, and the container and version-control skills every backend role assumes you have.
Move and shape data at scale.
SQL and pandas for the fundamentals, PySpark for volume, then cloud and containers for the pipelines that run it all in production.
Test software properly, not just manually.
Enough Python to write real test code, pytest for structure, Selenium for the browser, and API testing — the combination most SDET interviews probe.
Ship, run and scale what other people build.
Containers and orchestration first, since they're the common language, then the three major clouds and the system-design vocabulary to justify choices.
Build a whole product with Django or Flask.
Python and SQL, then a batteries-included framework, REST for the parts that talk to a frontend, and deployment to finish the loop.
Four weeks out from an interview? Start here.
The topics that come up in almost every backend or data interview, in the order they're usually asked. Pair it with mock interviews as you go.
Want a plan built around what you already know instead? Generate a personal roadmap — it rates your current skills and tells you where the gaps are.
