Supervised Learning
Supervised Learning is a technique within Machine Learning Algorithms, itself a branch of Artificial Intelligence in the field of Data Science. In this method, an algorithm learns from labeled training data to make predictions on new, unseen data. The term "supervised" refers to the process of guiding the model during its training phase by providing it with input-output pairs, allowing it to adjust its internal parameters and improve its predictive abilities. In Supervised Learning, there are typically two types of labeled data: categorical and continuous. Categorical labels are used for classification tasks where the algorithm must assign a class or group to new data based on patterns learned from the training set. Continuous labels, on the other hand, are utilized in regression problems where the goal is to predict a numerical value or quantity. Supervised Learning plays an essential role in various real-world applications, including image and speech recognition, natural language processing, and predictive modeling for business intelligence and decision making. By enabling machines to learn from existing data and make accurate predictions on new data points, Supervised Learning contributes significantly to the advancement of technology within Data Science and Artificial Intelligence.