Natural Language Processing with Python Quick Start Guide

More Information
Learn
  • Understand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpus
  • Work with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clustering
  • Deep Learning in NLP using PyTorch with a code-driven introduction to PyTorch
  • Using an NLP project management Framework for estimating timelines and organizing your project into stages
  • Hack and build a simple chatbot application in 30 minutes
  • Deploy an NLP or machine learning application using Flask as RESTFUL APIs
About

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 workflow 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.

Features
  • A no-math, code-driven programmer’s guide to text processing and NLP
  • Get state of the art results with modern tooling across linguistics, text vectors and machine learning
  • Fundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorch
Page Count 182
Course Length 5 hours 27 minutes
ISBN 9781789130386
Date Of Publication 30 Nov 2018
What is NLP?
NLP workflow template
Example – text classification workflow
Summary
What is deep learning?
Understanding deep learning
Putting it all together – the training loop
Kaggle – text categorization challenge
Summary

Authors

Nirant Kasliwal

Nirant Kasliwal maintains an awesome list of NLP natural language processing resources. GitHub's machine learning collection features this as the go-to guide. Nobel Laureate Dr. Paul Romer found his programming notes on Jupyter Notebooks helpful. Nirant won the first ever NLP Google Kaggle Kernel Award. At Soroco, image segmentation and intent categorization are the challenges he works with. His state-of-the-art language modeling results are available as Hindi2vec.