The core ideas behind machine learning, explained without the hype.
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.
What a model actually learns from data, the three main kinds of problem, and why a model that looks perfect often isn't.
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.
20 in this topic
A plain-language definition of AI, the difference between narrow and general AI, and why today's systems are mostly about learning patterns from data.
AI foundations 2 min read 8 Oct 2026 For people
AI, machine learning and deep learning are often used interchangeably, but they are nested ideas. Here is how they fit together.
AI foundations 2 min read 7 Oct 2026 For people
From the 1956 Dartmouth workshop to large language models: the booms, the 'AI winters' and the ideas that shaped the field.
AI foundations 2 min read 6 Oct 2026 For people
The core loop of machine learning — data, model, loss and optimisation — explained without equations.
AI foundations 2 min read 5 Oct 2026 For people
The three main ways machines learn: from labelled examples, from structure in unlabelled data, and from trial and reward.
AI foundations 2 min read 4 Oct 2026 For people
A model is the learned artefact at the heart of every AI system. Here is what it contains, how it is stored and how it is used.
AI foundations 2 min read 3 Oct 2026 For people
Why data is split into three parts, what each part is for, and the mistakes that make evaluation results meaningless.
AI foundations 2 min read 2 Oct 2026 For people
How to tell when a model has memorised noise or missed the pattern, and the standard ways to fix each.
AI foundations 2 min read 1 Oct 2026 For people
What features and labels are, how to choose them, and why feature quality often matters more than the algorithm.
AI foundations 2 min read 30 Sep 2026 For people
Neurons, layers, weights and activation functions: the building blocks of deep learning, explained simply.
AI foundations 2 min read 29 Sep 2026 For people
Parameters are learned from data; hyperparameters are chosen by you. Knowing the difference is key to tuning models well.
AI foundations 2 min read 28 Sep 2026 For people
How LLMs work, what 'next-token prediction' means, and why they can be both remarkably capable and confidently wrong.
AI foundations 2 min read 27 Sep 2026 For people
What tokens are, why models count in them, and how the context window limits what a language model can consider at once.
AI foundations 2 min read 26 Sep 2026 For people
Generative AI creates new text, images, audio and code. How it differs from predictive AI, and where it is genuinely useful.
AI foundations 2 min read 25 Sep 2026 For people
Short, plain-English definitions of forty terms you will meet when working with AI and machine learning.
AI foundations 2 min read 24 Sep 2026 For people
A realistic view of current AI capabilities and limits, to help separate genuine opportunities from hype.
AI foundations 1 min read 23 Sep 2026 For people
When hand-written rules beat machine learning, when they don't, and how the two work together.
AI foundations 2 min read 22 Sep 2026 For people
The stages of a typical AI project, from framing the problem to monitoring in production — and where projects usually go wrong.
AI foundations 1 min read 21 Sep 2026 For people
Questions to ask before buying an AI product: accuracy, data handling, testing on your data and total cost.
AI foundations 2 min read 20 Sep 2026 For people
A practical learning path for newcomers: the concepts to learn first, the tools to try and small projects to build.
AI foundations 2 min read 19 Sep 2026 For people