This is the code repository for Hands-On Python Natural Language Processing, published by Packt.
Explore tools and techniques to analyze and process text with a view to building real-world NLP applications
This book provides a blend of both the theoretical and practical aspects of Natural Language Processing (NLP). It covers the concepts essential to develop a thorough understanding of NLP and also delves into a detailed discussion on NLP based use-cases such as language translation, sentiment analysis, etc. Every module covers real-world examples.
This book covers the following exciting features:
- Understand how NLP powers modern applications
- Explore key NLP techniques to build your natural language vocabulary
- Transform text data into mathematical data structures and learn how to improve text mining models
- Discover how various neural network architectures work with natural language data
- Get the hang of building sophisticated text processing models using machine learning and deep learning
- Check out state-of-the-art architectures that have revolutionized research in the NLP domain
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
set_of_words = set()
for word in preprocessed_corpus[0].split():
set_of_words.add(word)
vocab = list(set_of_words)
print(vocab)
Following is what you need for this book: This NLP Python book is for anyone looking to learn NLP’s theoretical and practical aspects alike. It starts with the basics and gradually covers advanced concepts to make it easy to follow for readers with varying levels of NLP proficiency. This comprehensive guide will help you develop a thorough understanding of the NLP methodologies for building linguistic applications; however, working knowledge of Python programming language and high school level mathematics is expected.
With the following software and hardware list you can run all code files present in the book (Chapter 2-11).
| Chapter | Software required | OS required |
|---|---|---|
| 2 - 11 | Jupyter Notebook/Google Colab, Python 3.x, GPU (preferred) | Windows, macOS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
- Page 167:
from sklearn.naive_bayes import MultinomialNaive Bayes
clf = MultinomialNaive Bayes()**
should be
from sklearn.naive_bayes import MultinomialNB
clf = MultinomialNB()
-
Natural Language Processing with Python Quick Start Guide [Packt] [Amazon]
-
Deep Learning for Natural Language Processing [Packt] [Amazon]
Aman Kedia is a data enthusiast and lifelong learner. He is an avid believer in Artificial Intelligence (AI) and the algorithms supporting it. He has worked on state-of-the-art problems in Natural Language Processing (NLP), encompassing resume matching and digital assistants, among others. He has worked at Oracle and SAP, trying to solve problems leveraging advancements in AI. He has four published research papers in the domain of AI.
Mayank Rasu has more than 12 years of global experience as a data scientist and quantitative analyst in the investment banking industry. He has worked at the intersection of finance and technology and has developed and deployed AI-based applications within the finance domain. His experience includes building sentiment analyzers, robotics, and deep learning-based document review, among many others areas.
Click here if you have any feedback or suggestions.
If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.