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

Book Description

The landscape of has changed dramatically in the past few years. approaches now require mature tools like ’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 and a basic understanding of machine learning 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 design 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. Text Ingestion and Wrangling
Chapter 2. Machine Learning on Text

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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