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In den WarenkorbZustand: New. This practical guide shows programmers and data scientists who have an intermediate-level understanding of Python and a basic understanding of machine learning and natural language processing how to become more proficient in these two exciting areas of data science. Num Pages: 250 pages. BIC Classification: UN. Category: (P) Professional & Vocational. Dimension: 250 x 150 x 15. Weight in Grams: 666. . 2018. 1st Edition. Paperback. . . . .
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In den WarenkorbZustand: New. This practical book presents a data scientist s approach to building language-aware products with applied machine learning.Über den AutorrnrnBenjamin Bengfort is a Data Scientist who lives inside the beltway but ignores politics.
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In den WarenkorbTaschenbuch. Zustand: Neu. Neuware - This practical book presents a data scientist's approach to building language-aware products with applied machine learning.
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In den WarenkorbPaperback or Softback. Zustand: New. Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning 1.1. Book.
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In den WarenkorbZustand: New. This practical guide shows programmers and data scientists who have an intermediate-level understanding of Python and a basic understanding of machine learning and natural language processing how to become more proficient in these two exciting areas of data science. Num Pages: 250 pages. BIC Classification: UN. Category: (P) Professional & Vocational. Dimension: 250 x 150 x 15. Weight in Grams: 666. . 2018. 1st Edition. Paperback. . . . . Books ship from the US and Ireland.
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In den WarenkorbPaperback. Zustand: Brand New. 250 pages. 9.25x7.25x0.75 inches. In Stock.
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In den WarenkorbPaperback. Zustand: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Verlag: O'Reilly Media, Sebastopol, 2018
ISBN 10: 1491963042 ISBN 13: 9781491963043
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. 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" This practical book presents a data scientists approach to building language-aware products with applied machine learning. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Verlag: O'Reilly Media, Sebastopol, 2018
ISBN 10: 1491963042 ISBN 13: 9781491963043
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. 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" This practical book presents a data scientists approach to building language-aware products with applied machine learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.