This is the code repository for Natural Language Processing with Python Quick Start Guide, published by Packt.
Going from a Python developer to an effective Natural Language Processing Engineer
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
url = 'http://www.gutenberg.org/ebooks/1661.txt.utf-8'
file_name = 'sherlock.txt'
Following is what you need for this book: Programmers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.
With the following software and hardware list you can run all code files present in the book (Chapter 1-8).
| Chapter | Software required | OS required |
|---|---|---|
| 1 to 8 | conda with Python=3.6.x | Windows, 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.
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.
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.