Supervised learning is a fundamental machine learning paradigm in which algorithms learn from labeled training data to make predictions or decisions. In this approach, the model is provided with input-output pairs, allowing it to learn the mapping between the input features and the corresponding output labels. The goal is to minimize the error between predicted and actual outputs. Common applications of supervised learning include classification tasks (e.g., spam detection, sentiment analysis) and regression tasks (e.g., price prediction). Popular algorithms used in supervised learning include linear regression, decision trees, and support vector machines.