Authors:
- Covers NLP packages such as NLTK, gensim,and SpaCy
- Approaches topics such as "topic modeling" and "text summarization" in a beginner-friendly manner
- Explains how to ingest text data via web crawlers for use in deep learning NLP algorithms such as Word2Vec and Doc2Vec
Buy it now
Buying options
Tax calculation will be finalised at checkout
Other ways to access
This is a preview of subscription content, log in via an institution to check for access.
Table of contents (5 chapters)
-
Front Matter
-
Back Matter
About this book
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.
Authors and Affiliations
-
San Francisco, USA
Taweh Beysolow II
About the author
Bibliographic Information
Book Title: Applied Natural Language Processing with Python
Book Subtitle: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing
Authors: Taweh Beysolow II
DOI: https://doi.org/10.1007/978-1-4842-3733-5
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Taweh Beysolow II 2018
Softcover ISBN: 978-1-4842-3732-8Published: 12 September 2018
eBook ISBN: 978-1-4842-3733-5Published: 11 September 2018
Edition Number: 1
Number of Pages: XV, 150
Number of Illustrations: 32 b/w illustrations
Topics: Artificial Intelligence, Python, Open Source, Big Data