Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing

Beysolow II, Taweh

ISBN 10: 1484237323 / ISBN 13: 9781484237328

Published by Apress, 2018

About this Item

Seller Inventory # I-9781484237328

Bibliographic Details

• Title: Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing
• Publisher: Apress
• Publication Date: 2018
• Language: English
• ISBN 10: 1484237323
• ISBN 13: 9781484237328
• Binding: Soft cover
• Condition: New

About this title

Synopsis

Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. 

Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment.

What You Will Learn  

• Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim
  
• Manipulate and preprocess raw text data in formats such as .txt and .pdf
  
• Strengthen your skills in data science by learning both the theory and the application of various algorithms  

Who This Book Is For 

You should be at least a beginner in ML to get the most out of this text, but you needn’t feel that you need be an expert to understand the content.

About the Author

Taweh Beysolow II is a Machine Learning Scientist and Author currently based in the United States. He has a Bachelor of Science degree in Economics from St. Johns University and a Master of Science in Applied Statistics from Fordham University. His professional experience has included applying machine learning and natural language processing techniques to financial, text (structured and unstructured), and social media data.