Natural Language Processing Recipes

Unlocking Text Data with Machine Learning and Deep Learning Using Python

Authors: Kulkarni, Akshay, Shivananda, Adarsha

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  • Explains NLP concepts with simple programming recipes and implementation in Python
  • Teaches NLP life cycle end-to-end implementation pipeline: leverage state-of-the-art techniques and tools
  • Covers the latest NLP algorithms being implemented in the industry
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eBook $39.99
price for USA
  • ISBN 978-1-4842-7351-7
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $49.99
price for USA
  • ISBN 978-1-4842-7350-0
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
About this book

Focus on implementing end-to-end projects using Python and leverage state-of-the-art algorithms. This book teaches you to efficiently use a wide range of natural language processing (NLP) packages to: implement text classification, identify parts of speech, utilize topic modeling, text summarization, sentiment analysis, information retrieval, and many more applications of NLP. 
The book begins with text data collection, web scraping, and the different types of data sources. It explains how to clean and pre-process text data, and offers ways to analyze data with advanced algorithms. You then explore semantic and syntactic analysis of the text. Complex NLP solutions that involve text normalization are covered along with advanced pre-processing methods, POS tagging, parsing, text summarization, sentiment analysis, word2vec, seq2seq, and much more. The book presents the fundamentals necessary for applications of machine learning and deep learning in NLP. This second edition goes over advanced techniques to convert text to features such as Glove, Elmo, Bert, etc. It also includes an understanding of how transformers work, taking sentence BERT and GPT as examples. The final chapters explain advanced industrial applications of NLP with solution implementation and leveraging the power of deep learning techniques for NLP problems. It also employs state-of-the-art advanced RNNs, such as long short-term memory, to solve complex text generation tasks. 
After reading this book, you will have a clear understanding of the challenges faced by different industries and you will have worked on multiple examples of implementing NLP in the real world.


What You Will Learn

  • Know the core concepts of implementing NLP and various approaches to natural language processing (NLP), including NLP using Python libraries such as NLTK, textblob, SpaCy, Standford CoreNLP, and more
  • Implement text pre-processing and feature engineering in NLP, including advanced methods of feature engineering
  • Understand and implement the concepts of information retrieval, text summarization, sentiment analysis, text classification, and other advanced NLP techniques leveraging machine learning and deep learning


Who This Book Is For

Data scientists who want to refresh and learn various concepts of natural language processing (NLP) through coding exercises

About the authors

Akshay Kulkarni is an AI and machine learning evangelist and thought leader. He has consulted with Fortune 500 and global enterprises to drive AI and data science-led strategic transformations. He has a rich experience of building and scaling AI and machine learning businesses and creating significant client impact. Akshay is currently Manager-Data Science & AI at Publicis Sapient where he is part of strategy and transformation interventions through AI. He manages high-priority growth initiatives around data science, works on AI engagements, and applies state-of-the-art techniques. Akshay is a Google Developers Expert-Machine Learning, and is a published author of books on NLP and deep learning. He is a regular speaker at major AI and data science conferences, including Strata, O'Reilly AI Conf, and GIDS. In 2019, he was featured as one of the Top "40 under 40 Data Scientists" in India. In his spare time, he enjoys reading, writing, coding, and helping aspiring data scientists. He lives in Bangalore with his family.

Adarsha Shivananda is Lead Data Scientist at Indegene's Product and Technology team where he leads a group of analysts who enable predictive analytics and AI features for all of their healthcare software products. They handle multi-channel activities for pharma products and solve real-time problems encountered by pharma sales reps. Adarsha aims to build a pool of exceptional data scientists within the organization and to solve greater health care problems through training programs and staying ahead of the curve. His core expertise involves machine learning, deep learning, recommendation systems, and statistics. Adarsha has worked on data science projects across multiple domains using different technologies and methodologies. Previously, he was part of Tredence Analytics and IQVIA. He lives in Bangalore and loves to read and teach data science.

Table of contents (7 chapters)

Table of contents (7 chapters)

Buy this book

eBook $39.99
price for USA
  • ISBN 978-1-4842-7351-7
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $49.99
price for USA
  • ISBN 978-1-4842-7350-0
  • Free shipping for individuals worldwide
  • Institutional customers should get in touch with their account manager
  • Covid-19 shipping restrictions
  • Usually ready to be dispatched within 3 to 5 business days, if in stock
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Bibliographic Information

Bibliographic Information
Book Title
Natural Language Processing Recipes
Book Subtitle
Unlocking Text Data with Machine Learning and Deep Learning Using Python
Authors
Copyright
2021
Publisher
Apress
Copyright Holder
Akshay Kulkarni and Adarsha Shivananda
eBook ISBN
978-1-4842-7351-7
DOI
10.1007/978-1-4842-7351-7
Softcover ISBN
978-1-4842-7350-0
Edition Number
2
Number of Pages
XXVI, 283
Number of Illustrations
73 b/w illustrations
Topics