In this chapter, we built on our understanding of text vectorization, data preprocessing, and so on to gain an end-to-end understanding of applying ML algorithms to develop NLP applications. We learned about the additional pre-processing steps required for ML training and gained a thorough understanding of the Naive Bayes and SVM algorithms. We applied our understanding of text data processing and ML algorithms to build a sentiment analyzer and deployed the model to perform sentiment analysis in real-time. We also learned how to measure the performance of ML models and discussed some important dos and don'ts about building ML-based applications.
In the next chapter, we will learn how to apply deep learning to text processing and cover how neural networks can help us improve the accuracy of our applications.