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© 2019

Natural Language Processing Recipes

Unlocking Text Data with Machine Learning and Deep Learning using Python

  • Covers advanced programming recipes in natural language processing

  • Covers recent concepts such as RNN and embedding in national language processing

  • Includes many practical code examples using Python

Book

Table of contents

  1. Front Matter
    Pages i-xxv
  2. Akshay Kulkarni, Adarsha Shivananda
    Pages 1-35
  3. Akshay Kulkarni, Adarsha Shivananda
    Pages 37-65
  4. Akshay Kulkarni, Adarsha Shivananda
    Pages 67-96
  5. Akshay Kulkarni, Adarsha Shivananda
    Pages 97-128
  6. Akshay Kulkarni, Adarsha Shivananda
    Pages 129-183
  7. Akshay Kulkarni, Adarsha Shivananda
    Pages 185-227
  8. Back Matter
    Pages 229-234

About this book

Introduction

Implement natural language processing applications with Python using a problem-solution approach. This book has numerous coding exercises that will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identification, topic modeling, text summarization, and sentiment analysis. 

Natural Language Processing Recipes starts by offering solutions for cleaning and preprocessing text data and ways to analyze it with advanced algorithms. You’ll see practical applications of the semantic as well as syntactic analysis of text, as well as complex natural language processing approaches that involve text normalization, advanced preprocessing, POS tagging, parsing, text summarization, and sentiment analysis. You will also learn various applications of machine learning and deep learning in natural language processing.

By using the recipes in this book, you will have a toolbox of solutions to apply to your own projects in the real world, making your development time quicker and more efficient. 

You will:
  • Apply NLP techniques using Python libraries such as NLTK, TextBlob, spaCy, Stanford CoreNLP, and many more
  • Implement the concepts of information retrieval, text summarization, sentiment analysis, and other advanced natural language processing techniques.
  • Identify machine learning and deep learning techniques for natural language processing and natural language generation problems

Keywords

Natural language processing Text analytics NLP using python Machine learning Deep Learning Python Unstructured Data

Authors and affiliations

  1. 1.BangaloreIndia
  2. 2.BangaloreIndia

About the authors

Akshay Kulkarni is an AI and machine learning evangelist. Akshay has a rich experience of building and scaling AI and machine learning businesses and creating significant client impact. He is currently the senior data scientist at SapientRazorfish’s core data science team where he is part of strategy and transformation interventions through AI and works on various machine learning, deep learning and artificial intelligence engagements by applying state-of-the-art techniques in this space. Previously he was part of Gartner and Accenture, where he scaled the analytics and data science business. 

He is a regular speaker at major data science conferences. He is a visiting faculty for some of the top graduate institutes 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 a senior data scientist at Indegene's product and technology team where he is working on building machine learning and AI capabilities for pharma products. He is aiming to build a pool of exceptional data scientists within and outside of the organization to solve greater problems through brilliant training programs and always wants to stay ahead of the curve. Previously he worked with Tredence Analytics and IQVIA. Adarsha has worked extensively in the pharma, healthcare, retail, and marketing domains.

He lives in Bangalore and loves to read, ride, and teach data science.


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