Supervised learning…. Introduction and Explanation

Supervised learning is a type of machine learning where an algorithm is trained on a labeled dataset. In supervised learning, the algorithm is provided with input-output pairs, and it learns to predict the output for new inputs. The goal of supervised learning is to train the algorithm to generalize to new inputs and outputs beyond the training data. Supervised learning is often used in applications where there is a well-defined output variable, such as predicting stock prices, diagnosing diseases, or recognizing objects in images. It is an effective technique for solving a wide range of problems, including regression and classification tasks.