Supervised Learning is a type of machine learning where a model is trained on labeled data. This means that the input data is paired with the correct output, allowing the model to learn the relationship between them. Common applications include image recognition, spam detection, and medical diagnosis. In Supervised Learning, algorithms such as linear regression and decision trees are used to make predictions based on new, unseen data. The goal is to minimize the difference between the predicted outputs and the actual outputs, improving the model's accuracy over time.