Supervised Learning
Supervised Learning refers to a type of machine learning where an algorithm is trained on a labeled dataset, meaning each example in the training set has both input data and corresponding output labels. The model learns from this data to generalize and make predictions on new, unseen data. This approach contrasts with Unsupervised Learning, which deals with unlabeled data, requiring the algorithm to discover underlying patterns or structures without explicit guidance. In the context of Artificial Neural Networks within Artificial Intelligence, Supervised Learning serves as a fundamental method for training networks to perform various tasks, such as classification, regression, and prediction, by minimizing error between predicted outputs and actual labels. By leveraging labeled data, Supervised Learning enables more precise and accurate models, making it indispensable in numerous real-world applications across diverse industries, including Computer Vision, Natural Language Processing, and Recommendation Systems.