Preface

Natural language processing (NLP) is the use of machines to manipulate natural language. This book teaches you how to build NLP applications with code and relevant case studies using Python. This book will introduce you to the basic vocabulary and a suggested workflow for building NLP applications to help you get started with popular NLP tasks such as sentiment analysis, entity recognition, part of speech tagging, stemming, and word embeddings.

Who this book is for

This book is for programmers who wish to build systems that can interpret language and who have exposure to Python programming. A familiarity with NLP vocabulary and basics and machine learning would be helpful, but is not mandatory.

What this book covers

Chapter 1, Getting Started with Text Classification, introduces the reader to NLP and what a good NLP workflow looks like. You will also learn how to prepare text for machine learning with scikit-learn.

Chapter 2, Tidying Your Text, discusses some of the most common text pre-processing ideas. You will be introduced to spaCy and will learn how to use it for tokenization, sentence extraction, and lemmatization.

Chapter 3, Leveraging Linguistics, goes into a simple use case and examines how we can solve it. Then, we repeat this task again, but on a slightly different text corpus.

Chapter 4, Text Representations – Words to Numbers, introduces readers to the Gensim API. We will also learn to load pre-trained GloVe vectors and to use these vector representations instead of TD-IDF in any machine learning model.

Chapter 5, Modern Methods for Classification, looks at several new ideas regarding machine learning. The intention here is to demonstrate some of the most common classifiers. We will also learn about concepts such as sentiment analysis, simple classifiers, and how to optimize them for your datasets and ensemble methods.

Chapter 6, Deep Learning for NLP, cover what deep learning is, how it differs from what we have seen, and the key ideas in any deep learning model. We will also look at a few topics regarding PyTorch, how to tokenize text, and what recurrent networks are.

Chapter 7, Building Your Own Chatbot, explains why chatbots should be built and figures out the correct user intent. We will also learn in detail about intent , response, templates, and entities.

Chapter 8, Web Deployments, explains how to train a model and write some neater utils for data I/O. We are going to build a predict function and expose it using a Flask REST endpoint.

To get the most out of this book

• You will need conda with Python 3.6 or new version
• A basic understanding to Python programming language will be required
• NLP or machine learning experience will be helpful but is not mandatory

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