Summary
In this chapter, you learned about the different types of machine learning techniques, such as supervised and unsupervised learning. We explored unsupervised algorithms such as hierarchical clustering and k-means clustering, and supervised learning algorithms, such as k-nearest neighbor, the Naive Bayes classifier, and tree-based methods, such as random forest and XGBoost, that can perform both regression and classification. We discussed the need for sampling and went over different kinds of sampling techniques for splitting a given dataset into training and validation sets. Finally, we covered the process of saving a model on the hard disk and loading it back into memory for future use.
In the next chapter, you will learn about several techniques that you can use to collect data from various sources.