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

By Beysolow II, Taweh
Applied Natural Language Processing with Python:...

Details

Authors: Beysolow II, Taweh
Format: Paperback
GTIN13: 9781484237328
ISBN10: 1484237323
Page Count: 150
Dimensions: 6.14x0.36x9.21
Publisher: Apress
Jacket Notes: 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.
You will:
  • 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

Description

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.
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