Applied Text Analysis with Python: Enabling Language Aware Data Products with Machine Learning

Book Description

The programming landscape of natural language has changed dramatically in the past few years. approaches now require mature tools like Python’s scikit-learn to apply models to text at scale. This practical guide shows programmers and data scientists who have an intermediate-level understanding of Python and a basic understanding of and natural language how to become more proficient in these two exciting areas of data science.

This book presents a concise, focused, and applied approach to text with Python, and covers topics including text ingestion and wrangling, basic machine learning on text, classification for text , entity resolution, and text visualization. Applied Text with Python will enable you to and develop language-aware data products.

You’ll learn how and why machine learning algorithms make decisions about language to analyze text; how to ingest, wrangle, and preprocess language data; and how the three primary text analysis libraries in Python work in concert. Ultimately, this book will enable you to design and develop language-aware data products.

Table of Contents

Chapter 1. Language And Computation
Chapter 2. Building A Custom Corpus
Chapter 3. Corpus Preprocessing And Wrangling
Chapter 4. Text Vectorization And Transformation Pipelines
Chapter 5. Classification For Text Analysis
Chapter 6. Clustering For Text Similarity
Chapter 7. Context-Aware Text Analysis
Chapter 8. Text Visualization
Chapter 9. Analysis Of Text
Chapter 10. Chatbots
Chapter 11. Scaling Text With Multiprocessing And Spark
Chapter 12. Deep Learning And Beyond

Book Details

  • Title: Applied Text Analysis with Python: Enabling Language Aware Data Products with Machine Learning
  • Author: , ,
  • Length: 250 pages
  • Edition: 1
  • Language: English
  • Publisher:
  • Publication Date: 2017-05-25
  • ISBN-10: 1491963042
  • ISBN-13: 9781491963043
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UsersCloud Click to downloadEPUB4.407/03/2018
UsersCloud Click to downloadTrue PDF, EPUB17.210/11/2018
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