The core ideas behind machine learning, explained without the hype.
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 7 Oct 2026
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 6 Oct 2026
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 5 Oct 2026
The core loop of machine learning — data, model, loss and optimisation — explained without equations.
AI foundations 2 min read 4 Oct 2026
The three main ways machines learn: from labelled examples, from structure in unlabelled data, and from trial and reward.
AI foundations 2 min read 3 Oct 2026
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 2 Oct 2026
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 1 Oct 2026
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 30 Sep 2026
What features and labels are, how to choose them, and why feature quality often matters more than the algorithm.
AI foundations 2 min read 29 Sep 2026
Neurons, layers, weights and activation functions: the building blocks of deep learning, explained simply.
AI foundations 2 min read 28 Sep 2026
Parameters are learned from data; hyperparameters are chosen by you. Knowing the difference is key to tuning models well.
AI foundations 2 min read 27 Sep 2026
How LLMs work, what 'next-token prediction' means, and why they can be both remarkably capable and confidently wrong.
AI foundations 2 min read 26 Sep 2026
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 25 Sep 2026
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 24 Sep 2026
Short, plain-English definitions of forty terms you will meet when working with AI and machine learning.
AI foundations 2 min read 23 Sep 2026
A realistic view of current AI capabilities and limits, to help separate genuine opportunities from hype.
AI foundations 1 min read 22 Sep 2026
When hand-written rules beat machine learning, when they don't, and how the two work together.
AI foundations 2 min read 21 Sep 2026
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 20 Sep 2026
Questions to ask before buying an AI product: accuracy, data handling, testing on your data and total cost.
AI foundations 2 min read 19 Sep 2026
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 18 Sep 2026