Summary

In this chapter, we have seen many concepts and tools that are widely used in the NLP domain. All of these concepts are the basic building blocks of features engineering. You can use any of these techniques when you want to generate features in order to generate NLP applications. We have looked at how parse, POS taggers, NER, n-grams, and bag-of-words generate Natural Language-related features. We have also explored the how they are built and what the different ways to tweak some of the existing tools are in case you need custom features to develop NLP applications. Further, we have seen basic concepts of linear algebra, statistics, and probability. We have also seen the basic concepts of probability that will be used in ML algorithms in the future. We have looked at some cool concepts such as TF-IDF, indexing, ranking, and so on, as well as the language model as part...