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Hands-on AI and data courses built around the datasets and models on the hub — plus the courses from other providers that people most often recommend.
150 courses
What counts as attack surface, how to build an inventory that stays true, and why switching things off beats defending them.
How LLM agents call tools in a loop, how to design tools they use well, and how to keep agents safe and reliable.
A methodical way through an application: mapping, authentication flows, access control and business logic.
What a test can and cannot tell you, how work is scoped and authorised, and how findings turn into fixes.
How injection works, why filtering fails, and the design patterns that actually contain it.
What actually changes when a model joins a system, the attacks that follow from it, and how to threat model an AI feature before you ship it.
How neural networks turn inputs into predictions, how they learn with gradient descent, and how to train one in PyTorch.
How LLMs work, how to write prompts that get reliable results, and how to check what comes back.
Build, compare and evaluate a real classifier end to end, predicting penguin species from their measurements.
Read a dataset card, load the data with pandas, deal with missing values, and check you are allowed to use it.
What a model actually learns from data, the three main kinds of problem, and why a model that looks perfect often isn't.
What has to exist around a model before it can serve real traffic, and the order to build it in.