Unsupervised Learning and Clustering

In this section, we will provide a brief introduction to the unsupervised machine learning (ML) technique. Unsupervised learning is a type of ML algorithm used for grouping related data objects and finding hidden patterns by inferencing from unlabeled datasets, that is, a training set consisting of input data without labels.

Let's see a real-life example. Suppose you have a large collection of not-pirated-totally-legal MP3s in a crowded and massive folder on your hard drive. Now, what if you can build a predictive model that helps automatically group together similar songs and organize them into your favorite categories such as country, rap, and rock?

This is an act of assigning an item to a group so that an MP3 is added to the respective playlist in an unsupervised way. In Lesson 1, From Data to Decisions – Getting Started with TensorFlow, on classification, we assumed that you're given a training dataset of correctly labeled data. Unfortunately...