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)

Getting Started with Text Classification

There are several ways that you can learn new ideas and learn new skills. In an art class students study colors, but aren't allowed to actually paint until college. Sound absurd?

Unfortunately, this is how most modern machine learning is taught. The experts are doing something similar. They tell you that need to know linear algebra, calculus and deep learning. This is before they'll teach you how to use natural language Processing (NLP).

In this book, I want us to learn by teaching the the whole game. In every section, we see how to solve real-world problems and learn the tools along the way. Then, we will dig deeper and deeper into understanding how to make these toolks. This learning and teaching style is very much inspired by Jeremy Howard of fame.

The next focus is to have code examples wherever possible. This is to ensure that there is a clear and motivating purpose behind learning a topic. This helps us understand with intuition, beyond math formulae with algebraic notation.

In this opening chapter, we will focus on an introduction to NLP. And, then jump into a text classification example with code.

This is what our journey will briefly look like:

  • What is NLP?
  • What does a good NLP workflow look like? This is to improve your success rate when working on any NLP project.
  • Text classification as a motivating example for a good NLP pipeline/workflow.