Unsupervised learning

Unsupervised learning is a method in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. Within such an approach, a machine learning model tries to find any similarities, differences, patterns, and structure in data by itself. No prior human intervention is needed.

Other methods in the supervision spectrum are Reinforcement Learning where the machine is given only a numerical performance score as guidance, and Weak or Semi supervision where a small portion of the data is tagged, and Self Supervision.