8.1: Unsupervised Learning
Now that you have had a chance to understand and implement supervised data mining techniques, we can move on to unsupervised techniques. Unsupervised learning assumes no labels for training observations. We let the data tell us what its classification should be. This can be done using many approaches, but we will focus on clustering techniques in this course.
We will continue to use scikit-learn for implementations. As you can see, there are several methods contained within the module. This unit will focus on K-means and agglomerative clustering. Follow along with the code for implementing these methods and begin to get used to the new syntax. As the next sections unfold, the meaning of the instructions related to clustering will become clearer.