Practical Machine Learning

In this chapter, we focus on practical issues pertaining to machine learning and data science. For instance, what is the difference between a “train set” and a “test set”? What does “overfitting” and “generalization” mean, and why are these concepts important? Why do we apply regularization? What do we do with missing values and outliers?