In this chapter, you have learned about KNN and Naive Bayes techniques, which require somewhat a little less computational power. KNN in fact is called a lazy learner, as it does not learn anything apart from comparing with training data points to classify them into class. Also, you have seen how to tune the k-value using grid search technique. Whereas explanation has been provided for Naive Bayes classifier, NLP examples have been provided with all the famous NLP processing techniques to give you flavor of this field in a very crisp manner. Though in text processing, either Naive Bayes or SVM techniques could be used as these two techniques can handle data with high dimensionality, which is very relevant in NLP, as the number of word vectors are relatively high in dimensions and sparse at the same time.

In the next chapter, we will be discussing SVM and neural networks with introduction to deep learning models, as deep learning is becoming the next generation technology in implementing...