Unsupervised learning refers to the process of building machine learning models without using labeled training data. Unsupervised learning finds applications in diverse fields of study, including market segmentation, stock markets, natural language processing, computer vision, and so on.

In the previous chapters, we were dealing with data that had labels associated with it. When we have labeled training data, the algorithms learn to classify data based on those labels. In the real world, we might not always have access to labeled data. Sometimes, we just have a lot of data and we need to categorize it in some way. This is where unsupervised learning comes into picture. Unsupervised learning algorithms attempt to build learning models that can find subgroups within the given dataset using some similarity metric.

Let's see how we formulate the learning problem in unsupervised learning. When we have a dataset without any labels, we assume that the data is generated...