What is unsupervised learning?
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, and computer vision, to name a few.
In the previous chapters, we were dealing with data that had labels associated with it. When we have labeled training data, algorithms learn to classify data based on those labels. In the real world, labeled data might not always be available.
Sometimes, a large quantity of data exists without labeling and it needs to be categorized in some way. This is the perfect use case for unsupervised learning. Unsupervised learning algorithms attempt to classify data into subgroups within a given dataset using some similarity metric.
When we have a dataset without any labels, we assume that the data is generated because of latent variables that govern the...
