Unsupervised learning is one of the most important branches of machine learning. It enables us to make predictions when we don't have target labels. In unsupervised learning, the model learns only via features because the dataset doesn't have a target label column. Most machine learning problems start with something that helps automate the process. For example, when you want to develop a prediction model for detecting diabetes patients, you need a set of target labels for each patient in your dataset. In the initial stages, arranging target labels for any machine learning problem is not an easy task, because it requires changing the business process to get the labels, whether by manual in-house labeling or collecting the data again with labels.
In this chapter, our focus is on learning about unsupervised learning techniques...
