In this chapter, we will explore some of the backbone libraries of Python for machine learning and natural language processing. Until now, we have used NLTK, Scikit, and genism, which had very abstract functions, and were very specific to the task in hand. Most of statistical NLP is heavily based on the vector space model, which in turn depends on basic linear algebra covered by NumPy. Also many NLP tasks, such as POS or NER tagging, are really classifiers in disguise. Some of the libraries we will discuss are heavily used in all these tasks.
The idea behind this chapter is to give you a quick overview of some the most fundamental Python libraries. This will help us understand more than just the data structure, design, and math behind some of the coolest libraries, such as NLTK and Scikit, which we have discussed in the previous chapters.
We will look at the following four libraries. I have tried to keep it short, but I highly encourage you...