Machine Learning for Mobile

Unsupervised learning

In this learning pattern, there is no supervision done to the model to make it learn. The model learns by itself based on the data fed to it and provides us with patterns it has learned. It doesn't predict any discrete categorical value or a continuous value, but rather provides the patterns it has understood by looking at the data fed into it. The training data fed in is unlabeled and doesn't provide sufficient knowledge information for the model to learn. 

Here, there's no supervision at all; actually, the model might be able to teach us new things after it learns the data. These algorithms are very useful where a feature set is too large and the human user doesn't know what to look for in the data.

This class of algorithms ...