Unsupervised learning is a paradigm in machine learning where we build models without relying on labeled training data. Up to this point, we have dealt with data that was labeled in some way. This means that learning algorithms can look at this data and learn to categorize it them based on labels. In the world of unsupervised learning, we don't have this opportunity! These algorithms are used when we want to find subgroups within datasets using a similarity metric.
In unsupervised learning, information from the database is automatically extracted. All this takes place without prior knowledge of the content to be analyzed. In unsupervised learning, there is no information on the classes that the examples belong to, or on the output corresponding to a given input. We want a model that can discover interesting properties, such as groups with similar characteristics...
