Introduction to Supervised Learning ¶ the task of supervised learning is as follows: Given a training set of N example input-output pairs (x1,y1), (x2,y2), … (xN,yN) (x 1, y 1), (x 2, y 2), (x N, y N) ...

Machine learning can be supervised, unsupervised, or semi-supervised. In supervised learning, models are trained on labeled data, meaning the input data is paired with the correct output.

Unsupervised learning eliminates the need for human input in creation of the AI engine. It uses unlabeled data and derives the underlying semantics and patterns which are then used to make decisions.