Supervised learning is a machine learning paradigm where the algorithm learns from labeled training data in order to make predictions or decisions. In this approach, the algorithm is provided with input-output pairs, and its goal is to learn a mapping from inputs to outputs by finding patterns or relationships in the training data. The algorithm then uses this learned mapping to make predictions on new, unseen data. Supervised learning is commonly used in various applications, such as classification, regression, and forecasting. In classification tasks, the algorithm predicts the class or category of a given input, while in regression tasks, the algorithm predicts a continuous value. Supervised learning is widely used in fields like image and speech recognition, natural language processing, and medical diagnosis.