Learning path: Machine learning

Your path

1. AI basicsDirect prerequisite

   Artificial intelligence (AI) is the field of building machine-based systems that, for a given set of objectives, produce outputs such as predictions, recommendations or decisions. Early AI tried to write intelligence down as hand-made rules; much of modern AI instead learns patterns from data. Today's systems can be remarkably strong at some tasks and surprisingly weak at others, so knowing what an AI system was built and tested to do matters as much as knowing what it can do.

   Take the AI basics quiz

2. Machine learningYour goal

   Machine learning is the part of AI in which a program improves at a task through experience — data — instead of following rules written in advance. Supervised learning learns from labelled examples, unsupervised learning finds structure in unlabelled data, and reinforcement learning learns which actions earn reward by trial and error. The real test of a learned model is not how well it fits its training data but how well it performs on new data it has never seen.

   Take the Machine learning quiz

Where to go next

Concepts that build directly on Machine learning:

• Gradient descent: Gradient descent is the step-by-step method most machine-learning systems use to learn: it measures how the error changes as each parameter changes (the gradient) and nudges every parameter a little way downhill. The learning rate sets the size of each step — too small and learning crawls, too large and it overshoots and can blow up. Variants such as stochastic gradient descent, momentum and Adam make it practical for models with millions of parameters.
• Neural networks: An artificial neural network is a mathematical function built from many simple units arranged in layers: each unit multiplies its inputs by weights, adds a bias and passes the total through an activation function. Stacking layers lets the network represent patterns — such as XOR — that no single linear unit can. The weights are not written by hand but learned from data, usually by back-propagation and gradient descent; the networks are inspired by the brain but are not realistic models of it.
• Reinforcement learning: Reinforcement learning (RL) is learning by trial and error: an agent takes actions in an environment, receives numerical rewards, and gradually learns which actions lead to the most reward over time. Unlike supervised learning, nobody tells the agent the right action — it must explore, and rewards may arrive long after the actions that earned them. RL produced landmark results in Atari games and Go and is used to fine-tune language models from human feedback, but an agent optimises exactly the reward it is given, which may not be what its designers meant.
• Training data: Training data is the set of examples a machine-learning model learns from, and it shapes everything the model can and cannot do. Data is usually split into training, validation and test sets so that the model is judged on examples it never learned from. Gaps, mislabelled examples and skewed samples in the data become gaps, errors and biases in the model, which is why datasets need documenting and checking like any other scientific instrument.