This book, subtitled "Enabling Language-Aware Data Products with Machine Learning", presents a data scientist’s approach to building language-aware products with applied machine learning. Authors Benjamin Bengfort, Dr. Rebecca Bilbro and Tony Ojeda demonstrate 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. The applied nature of the book means that the authors focus not on the academic nature of linguistics or statistical models, but instead on how to be effective at deploying models trained on text inside of a software application.

Authors: Benjamin Bengfort, Dr. Rebecca Bilbro and Tony Ojeda
Publisher: O'Reilly
Date: July 2018
Pages: 332
ISBN: 978-1491963043
Print: 1491963042
Kindle: B07DNKHJL8
Audience: Python developers
Level: Intermediate/Advanced
Category: Artificial Intelligence, Python, Data Science

• 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.

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