Learning from examples
A machine learning model finds patterns in example data and uses them to make predictions about new data it hasn’t seen.
Training and testing
Data is split so a model is trained on one part and evaluated on another. This shows whether it generalises or simply memorises.
Common model types
Regression predicts numbers, classification predicts categories and clustering groups similar items. Neural networks can handle all three at scale.
Limits to keep in mind
Models reflect their training data, including its gaps and biases. Evaluating results carefully is as important as building the model.