The Different Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning

Supervised learning: Supervised learning involves training a machine learning model on labeled data, which includes both input data and the corresponding correct output. The goal of supervised learning is to predict the output for new, unseen input data based on the patterns learned from the labeled training data. Examples of supervised learning tasks include classification (predicting a categorical output) and regression (predicting a continuous output). Unsupervised learning: In unsupervised learning, the machine learning model is not provided with labeled training data. Instead, the model must discover patterns and relationships in the data on its own.