Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning
Benjamin Bengfort,Rebecca Bilbro,Tony Ojeda
Language: English
Published by Oâ Reilly, 2018
- Softcover
- Used
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Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .
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- Title
- Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning
- Author
- Benjamin Bengfort,Rebecca Bilbro,Tony Ojeda
- Publisher
- Oâ Reilly
- Publication year
- 2018
- Condition
- Very Good
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1491963042
- ISBN 13
- 9781491963043
From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist's approach to building language-aware products with applied machine learning.
You'll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you'll be equipped with practical methods to solve any number of complex real-world problems.
- Preprocess and vectorize text into high-dimensional feature representations
- Perform document classification and topic modeling
- Steer the model selection process with visual diagnostics
- Extract key phrases, named entities, and graph structures to reason about data in text
- Build a dialog framework to enable chatbots and language-driven interaction
- Use Spark to scale processing power and neural networks to scale model complexity
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Rebecca Bilbro is a data scientist, Python programmer, and author in Washington, DC. She specializes in data visualization for machine learning, from feature analysis to model selection and hyperparameter tuning. She is an active contributor to the open source community and has conducted research on natural language processing, semantic network extraction, entity resolution, and high dimensional information visualization. She earned her doctorate from the University of Illinois, Urbana-Champaign, where her research centered on communication and visualization practices in engineering.
Tony is the founder of District Data Labs and focuses on applied analytics for business strategy. He has published a book on practical data science, and has experience with hands-on education and data science curricula.
"About the title" may belong to another edition of this title.
AwesomeBooks
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