What is the difference between supervised, unsupervised, and reinforcement learning?

These are the three main types of machine learning:

• Supervised Learning is when the model is trained on a labeled dataset—meaning we know the input and the expected output. The goal is for the model to learn a mapping from inputs to outputs. Common examples include classification and regression tasks, like spam detection or predicting house prices.

• Unsupervised Learning deals with data that has no labels. The model tries to identify hidden patterns or groupings in the data. Clustering and dimensionality reduction are typical examples—like customer segmentation or topic modeling.

• Reinforcement Learning is quite different. Here, an agent learns by interacting with an environment. It receives rewards or penalties based on its actions and learns to maximize cumulative reward over time. It’s commonly used in areas like robotics, game AI, and recommendation systems.

In short:

• Supervised = Learn from labeled data

• Unsupervised = Find patterns in unlabeled data

• Reinforcement = Learn through trial and error using feedback from actions