Summary
In this chapter, you learned about different types of machine learning techniques, such as supervised and unsupervised learning. Various kinds of supervised learning algorithms, such as K-Nearest Neighbor and a Naive Bayes classifier have been explored. Moreover, different kinds of sampling techniques for splitting a given dataset into training and validation sets have also been elucidated with examples. This chapter focused mainly on developing machine learning models using features extracted from text data.
As you progressed through the chapter, you were introduced to various metrics used for evaluating the performance of these models. Finally, we covered the process of saving a model on the hard disk and loading it back to the memory for future use.
In the next chapter, you will learn several techniques with which data can be collected from various sources.