Supervised learning is a branch of machine learning where the model is trained on labeled data, meaning each training example is paired with an output label. It encompasses two primary types: regression and classification. Regression is used for predicting continuous outcomes, such as forecasting stock prices or estimating housing values. It involves modeling the relationship between input features and a continuous target variable. Classification, on the other hand, is aimed at predicting discrete categories or classes. For example, it can be used to categorize emails as spam or not spam, or to identify whether an image contains a cat or a dog. Both regression and classification leverage labeled data to learn patterns and make predictions, but they differ in the nature of the target variable and the methods used to evaluate their performance.