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This practical book presents a data scientist's approach to building language-aware products with applied machine learning.
About the author
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.
Summary
This practical book presents a data scientist’s approach to building language-aware products with applied machine learning.
