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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.
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What a model actually learns from data, the three main kinds of problem, and why a model that looks perfect often isn't.
Loss, gradients and generalisation — the mechanism, without the mathematics being the point.
Turning things into numbers that carry meaning, and what you can do with them.
Attention, tokens and the architecture behind current language models — enough to reason about behaviour.
Model, parameter, inference, context, temperature — the terms, used correctly.
Decide where AI genuinely helps, whether to build or buy, how to measure success, and what could go wrong — before anyone writes code.
Harvard's CS50 AI: search, knowledge, uncertainty, optimisation, learning, neural networks and language, through Python projects.
A non-technical course by Andrew Ng on what AI can and can't do, and how organisations adopt it.
A free introduction to AI for non-experts — no programming or complicated maths required.