In the previous chapter, you learned how to find the classes or categories of individual datapoints. With a handful of training data items that were paired with their respective classes, you learned a model, which we can now use to classify future data items. We called this supervised learning because the learning was guided by a teacher; in our case, the teacher had the form of correct classifications.
Let's now imagine that we do not possess those labels by which we can learn the classification model. This could be, for example, because they were too expensive to collect. Just imagine the cost if the only way to obtain millions of labels will be to ask humans to classify those manually. What could we have done in that case?
Well, of course, we will not be able to learn a classification model. Still, we could find some pattern within the data itself. That is, let the data describe itself. This is what we will do in this chapter, where we consider...