5.1.1. Supervised learning

In supervised learning, the data is tagged with the correct values for prediction/classification associated to it. The algorithm learns by minimizing the error between its results and the correct results. This is the most common form of machine learning and the easiest, however labeled data is not easy to come by as its curation and tagging is usually expensive.

Neural networks, support vector machines (SVMs), linear/logistic regression and decision trees are a few examples of supervised learning algorithms and the applications could be classification of images, weather prediction, sentiment detection, face recognition, etc.
