In this chapter, we looked at several new ideas regarding machine learning. The intention here was to demonstrate some of the most common classifiers. We looked at how to use them with one thematic idea: translating text to a numerical representation and then feeding that to a classifier.
This chapter covered a fraction of the available possibilities. Remember, you can try anything from better feature extraction using Tfidf to tuning classifiers with GridSearch and RandomizedSearch, as well as ensembling several classifiers.
This chapter was mostly focused on pre-deep learning methods for both feature extraction and classification.
Note that deep learning methods also allow us to use a single model where the feature extraction and classification are both learned from the underlying data distribution. While a lot has been written about deep learning in computer vision...