Mastering Machine Learning with scikit-learn - Second Edition

Summary

In this chapter, we defined machine learning as the design of programs that can improve their performance at a task by learning from experience. We discussed the spectrum of supervision in experience. At one end is supervised learning, in which a program learns from inputs that are labeled with their corresponding outputs. Unsupervised learning, in which the program must discover structure in only unlabeled inputs, is at the opposite end of the spectrum. Semi-supervised approaches make use of both labeled and unlabeled training data.

Next we discussed common types of machine learning tasks and reviewed examples of each. In classification tasks the program predict the value of a discrete response variable from the observed explanatory ...