paperback. Condizione: Acceptable.
Da: Goodwill Books, Hillsboro, OR, U.S.A.
Condizione: good. Signs of wear and consistent use.
Paperback. Condizione: Good. No Jacket. Former library book; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.
Paperback. Condizione: 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!
Da: Textbooks_Source, Columbia, MO, U.S.A.
Prima edizione
paperback. Condizione: Good. 1st Edition. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
Condizione: new.
Condizione: New.
Condizione: As New. Unread book in perfect condition.
Condizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
EUR 42,42
Quantità: 1 disponibili
Aggiungi al carrelloPAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Lingua: Inglese
Editore: O'Reilly Media, Sebastopol, 2018
ISBN 10: 1491963042 ISBN 13: 9781491963043
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Paperback. Condizione: 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.
Paperback or Softback. Condizione: New. Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning. Book.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 42,41
Quantità: 5 disponibili
Aggiungi al carrelloCondizione: New.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 48,10
Quantità: 2 disponibili
Aggiungi al carrelloCondizione: New. In.
EUR 61,25
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: New. 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.
Condizione: New.
EUR 45,87
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: New.
Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
Prima edizione
EUR 53,60
Quantità: 2 disponibili
Aggiungi al carrelloCondizione: 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. . . . .
EUR 65,96
Quantità: Più di 20 disponibili
Aggiungi al carrelloPaperback. Condizione: New. 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.
Da: California Books, Miami, FL, U.S.A.
EUR 66,49
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 49,72
Quantità: 5 disponibili
Aggiungi al carrelloCondizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 50,40
Quantità: 5 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
EUR 50,72
Quantità: 1 disponibili
Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
Condizione: 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.
Da: Studibuch, Stuttgart, Germania
EUR 11,27
Quantità: 1 disponibili
Aggiungi al carrellopaperback. Condizione: Gut. 310 Seiten; 9781491963043.3 Gewicht in Gramm: 1.
EUR 43,22
Quantità: 2 disponibili
Aggiungi al carrelloCondizione: NEW.
Da: LiLi - La Liberté des Livres, CANEJAN, Francia
EUR 31,56
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: fine. edition 2018. l'article peut presenter de tres legers signes d'usure, petites rayures ou imperfections esthetiques. vendeur professionnel; envoi soigne en 24/48h.
Da: Revaluation Books, Exeter, Regno Unito
EUR 88,72
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 250 pages. 9.25x7.25x0.75 inches. In Stock.
EUR 57,74
Quantità: 2 disponibili
Aggiungi al carrelloCondizione: 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.
Paperback. Condizione: New. 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.