Supervised learning algorithms are trained using labelled data, where each input is paired with the correct output. These algorithms learn to make predictions by identifying patterns within the data. Applications include image recognition, fraud detection, and predictive maintenance.

Unsupervised learning algorithms detect patterns and structures within data that lack labels. They group data based on similarities and differences without prior knowledge of the outcomes. Applications include customer segmentation, anomaly detection, and recommendation systems.

Reinforcement learning algorithms improve their performance by learning through trial and error, aiming to maximise rewards over time. These algorithms make decisions based on feedback from their actions. Applications include robotics, game-playing, and autonomous vehicles.