In this chapter, we saw some interesting machine learning techniques. We covered one of the fundamental concepts behind machine learning called train/test. We saw how to use train/test to try to find the right degree polynomial to fit a given set of data. We then analyzed the difference between supervised and unsupervised machine learning.
We saw how to implement a spam classifier and enable it to determine whether an email is spam or not using the Naive Bayes technique. We talked about k-means clustering, an unsupervised learning technique, which helps group data into clusters. We also looked at an example using scikit-learn which clustered people based on their income and age.
We then went on to look at the concept of entropy and how to measure it. We walked through the concept of decision trees and how, given a set of training data, you can actually get Python to generate...
