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)

Understanding deep learning

In a loosely worded manner, machine learning is about mapping inputs (such as images, or movie reviews) to targets (such as the label cat or positive). The model does this by looking at (or training from) several pairs of input and targets.

Deep neural networks do this input-to-target mapping using a long sequence of simple data transformations (layers). This sequence length is referred to as the depth of the network. The entire sequence from input-to-target is referred to as a model that learns about the data. These data transformations are learned by repeated observation of examples. Let's look at how this learning happens.

Puzzle pieces

We are looking at a particular subclass of challenges...