Book Image

Natural Language Processing with Python Quick Start Guide

By : Nirant Kasliwal
Book Image

Natural Language Processing with Python Quick Start Guide

By: Nirant Kasliwal

Overview of this book

NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a work?ow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges.
Table of Contents (10 chapters)

Summary

In this chapter, you got a feel for the broader things we need to make the project work. We saw the steps that are involved in this process by using a text classification example. We saw how to prepare text for machine learning with scikit-learn. We saw Logistic Regression for ML. We also saw a confusion matrix, which is a quick and powerful tool for making sense of results in all machine learning, beyond NLP.

We are just getting started. From here on out, we will dive deeper into each of these steps and see what other methods exist out there. In the next chapter, we will look at some common methods for text cleaning and extraction. Since this is what we will spend up to 80% of our total time on, it's worth the time and energy learning it.