Classification and regression are two fundamental types of supervised learning techniques in machine learning. Classification involves predicting a discrete label or category for a given input, such as determining whether an email is 'spam' or 'not spam'. In contrast, regression focuses on predicting a continuous value, like forecasting house prices based on various features such as size and location. Both methods utilize training data to learn patterns and make predictions. While classification is used for tasks with distinct categories, regression is applied when the output is a numerical value. Together, they form the backbone of many predictive analytics applications.