Unsupervised Learning

Unsupervised learning methods do not require labeled data for training a model, making predictions or inference. Therefore, datasets for unsupervised learning only consist of numerical features but do not contain any target values. In this chapter, we discuss two large classes of methods: clustering methods that try to find data points that “have something in common” and dimensionality reduction methods, which transform the data into another representation that might be significantly “simpler” or that reveal certain features of the dataset.