Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning (Paperback or Softback)

Bengfort, Benjamin

ISBN 10: 1491963042 / ISBN 13: 9781491963043

Editore: O'Reilly Media 7/6/2018, 2018

Riguardo questo articolo

Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning 1.1.

Codice articolo BBS-9781491963043

Dati bibliografici

• Titolo: Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning (Paperback or Softback)
• Editore: O'Reilly Media 7/6/2018
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 1491963042
• ISBN 13: 9781491963043
• Rilegatura: Paperback or Softback
• Condizione: New
• Peso: 1,1 libbre
• Tipo di libro: Book

Descrizione articolo

Riassunto

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

Informazioni sull?autore

Benjamin Bengfort is a Data Scientist who lives inside the beltway but ignores politics (the normal business of DC) favoring technology instead. He is currently working to finish his PhD at the University of Maryland where he studies machine learning and distributed computing. His lab does have robots (though this field of study is not one he favors) and, much to his chagrin, they seem to constantly arm said robots with knives and tools; presumably to pursue culinary accolades. Having seen a robot attempt to slice a tomato, Benjamin prefers his own adventures in the kitchen where he specializes in fusion French and Guyanese cuisine as well as BBQ of all types. A professional programmer by trade, a Data Scientist by vocation, Benjamin's writing pursues a diverse range of subjects from Natural Language Processing, to Data Science with Python to analytics with Hadoop and Spark.

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.