Supervised Learning#
Supervised learning is a type of machine learning in which a model is trained on labeled data, meaning that the correct output (also known as the ground truth) is provided for each example in the training data. The goal of supervised learning is to build a model that can make predictions on new, unseen data based on the patterns it has learned from the training data. For example, a supervised learning model could be trained on a dataset of images and their corresponding labels (e.g., “cat” or “dog”) and then be used to classify new images as they come in.