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Book details
File Size: 17 MB
Format: pdf
Print Length: 700 pages
Publisher: Apress; 2 edition (May 21, 2019)
Publication Date: May 21, 2019
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07S5QNB6W
Who This Book Is For
IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.
About this Book
Leverage Natural Language Processing (NLP) fundamentals in Python and learn how to set up your own robust environment for performing text analytics. This updated version will show you how to use the latest state-of-the-art frameworks and how to work with text data in Python.
You’ll explore several new topics, including working with Pythonfor NLP, illustrated with more hands-on examples. There are also new chapters on engineering text data (both traditional and newer deep learning based embedding methods) and deep learning for advanced text analytics and NLP.
While the overall structure of the book remains the same, the entire code base, modules, and frameworks will be updated to the latest Python 3.x release. You’ll review new and improved methods for evaluating and interpreting classification models, and will look at newer lexicons and methodologies for unsupervised learning.
What You’ll Learn
– Understand NPL and text syntax, semantics and structure
– Discover text cleaning and feature engineering-Review text classification and text clustering
– Assess text summarization and topic models
– Study deep learning for NLP
Book details
File Size: 17 MB
Format: pdf
Print Length: 700 pages
Publisher: Apress; 2 edition (May 21, 2019)
Publication Date: May 21, 2019
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B07S5QNB6W
Who This Book Is For
IT professionals, data analysts, developers, linguistic experts, data scientists and engineers and basically anyone with a keen interest in linguistics, analytics and generating insights from textual data.
About this Book
Leverage Natural Language Processing (NLP) fundamentals in Python and learn how to set up your own robust environment for performing text analytics. This updated version will show you how to use the latest state-of-the-art frameworks and how to work with text data in Python.
You’ll explore several new topics, including working with Pythonfor NLP, illustrated with more hands-on examples. There are also new chapters on engineering text data (both traditional and newer deep learning based embedding methods) and deep learning for advanced text analytics and NLP.
While the overall structure of the book remains the same, the entire code base, modules, and frameworks will be updated to the latest Python 3.x release. You’ll review new and improved methods for evaluating and interpreting classification models, and will look at newer lexicons and methodologies for unsupervised learning.
What You’ll Learn
– Understand NPL and text syntax, semantics and structure
– Discover text cleaning and feature engineering-Review text classification and text clustering
– Assess text summarization and topic models
– Study deep learning for NLP