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Practical Machine Learning Streaming by Putatunda Sayan (21 results)
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Condition: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!
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Condition: As New. Unread book in perfect condition.
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Practical Machine Learning for Streaming Data with Python: Design, Develop, and Validate Online or Incremental Machine Learning Models in a Streaming Data Context
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Practical Machine Learning for Streaming Data with Python : Design, Develop, and Validate Online Learning Models
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Practical Machine Learning for Streaming Data with Python : Design, Develop, and Validate Online Learning Models
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Practical Machine Learning for Streaming Data with Python: Design, Develop, and Validate Online Learning Models
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Practical Machine Learning for Streaming Data with Python: Design, Develop, and Validate Online Learning Models
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Condition: New. 1st ed. edition NO-PA16APR2015-KAP.
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Taschenbuch. Condition: Neu. Practical Machine Learning for Streaming Data with Python | Design, Develop, and Validate Online Learning Models | Sayan Putatunda | Taschenbuch | xvi | Englisch | 2021 | Apress | EAN 9781484268667 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Condition: new. Questo è un articolo print on demand.
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Practical Machine Learning for Streaming Data with Python
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights.You'll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You'll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow.Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. The book concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more.What You'll LearnUnderstand machine learning with streaming data conceptsReview incremental and online learningDevelop models for detecting concept driftExplore techniques for classification, regression, and ensemble learning in streaming data contextsApply best practices for debugging and validating machine learning models in streaming data contextGet introduced to other open-source frameworks for handling streamingdata.Who This Book Is ForMachine learning engineers and data science professionals 136 pp. Englisch.
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Explains the latest Scikit-Multiflow framework in detailExplains Supervised and Unsupervised Learning for streaming data One of the first books in the market on machine learning models for streaming data us.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights.You'll start with an introduction to streaming data, the various challenges associated with it, some of its real-world business applications, and various windowing techniques. You'll then examine incremental and online learning algorithms, and the concept of model evaluation with streaming data and get introduced to the Scikit-Multiflow framework in Python. This is followed by a review of the various change detection/concept drift detection algorithms and the implementation of various datasets using Scikit-Multiflow.Introduction to the various supervised and unsupervised algorithms for streaming data, and their implementation on various datasets using Python are also covered. The book concludes by briefly covering other open-source tools available for streaming data such as Spark, MOA (Massive Online Analysis), Kafka, and more.What You'll LearnUnderstand machine learning with streaming data conceptsReview incremental and online learningDevelop models for detecting concept driftExplore techniques for classification, regression, and ensemble learning in streaming data contextsApply best practices for debugging and validating machine learning models in streaming data contextGet introduced to other open-source frameworks for handling streamingdata.Who This Book Is ForMachine learning engineers and data science professionals.
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Practical Machine Learning for Streaming Data with Python
Language: English
Published by Apress, Apress Apr 2021, 2021
ISBN 10: 1484268660 ISBN 13: 9781484268667
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 136 pp. Englisch.
