In this chapter, we introduced the essentials of machine learning. We started with some easy, but still quite effective, classifiers (linear and logistic regressors, Naive Bayes, and K-Nearest Neighbors). Then, we moved on to the more advanced ones (SVM). We explained how to compose weak classifiers together (ensembles, Random Forests, and Gradient Tree Boosting). Finally, we had a peek at the algorithms used in big data, clustering, and deep learning.
In the next chapter, you'll be introduced to graphs, which is an interesting deviation from the predictors/target flat matrices. It is quite a hot topic in data science now. Expect to delve into very complex and intricate networks!