Published by O'Reilly Media, Incorporated, 2015
ISBN 10: 1491912766 ISBN 13: 9781491912768
Language: English
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Published by O'Reilly Media, Incorporated, 2015
ISBN 10: 1491912766 ISBN 13: 9781491912768
Language: English
Seller: Better World Books: West, Reno, NV, U.S.A.
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Published by O'Reilly Media, Incorporated, 2017
ISBN 10: 1491972955 ISBN 13: 9781491972953
Language: English
Seller: Better World Books, Mishawaka, IN, U.S.A.
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Published by O'Reilly Media 20/04/2015, 2015
ISBN 10: 1491912766 ISBN 13: 9781491912768
Language: English
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Published by O'Reilly Media 20/04/2015, 2015
ISBN 10: 1491912766 ISBN 13: 9781491912768
Language: English
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Add to basketPaperback. Condition: New. In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming. You'll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques-including classification, clustering, collaborative filtering, and anomaly detection-to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you'll find the book's patterns useful for working on your own data applications. With this book, you will: Familiarize yourself with the Spark programming model Become comfortable within the Spark ecosystem Learn general approaches in data science Examine complete implementations that analyze large public data sets Discover which machine learning tools make sense for particular problems Acquire code that can be adapted to many uses.
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Add to basketPaperback. Condition: New. The amount of data being generated today is staggering--and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming.Data scientists Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills offer an introduction to the Spark ecosystem, then dive into patterns that apply common techniques--including classification, clustering, collaborative filtering, and anomaly detection--to fields such as genomics, security, and finance. This updated edition also covers NLP and image processing.If you have a basic understanding of machine learning and statistics and you program in Python, this book will get you started with large-scale data analysis.Familiarize yourself with Spark's programming model and ecosystemLearn general approaches in data scienceExamine complete implementations that analyze large public datasetsDiscover which machine learning tools make sense for particular problemsExplore code that can be adapted to many uses.
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Paperback. Condition: New. The amount of data being generated today is staggering--and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming.Data scientists Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills offer an introduction to the Spark ecosystem, then dive into patterns that apply common techniques--including classification, clustering, collaborative filtering, and anomaly detection--to fields such as genomics, security, and finance. This updated edition also covers NLP and image processing.If you have a basic understanding of machine learning and statistics and you program in Python, this book will get you started with large-scale data analysis.Familiarize yourself with Spark's programming model and ecosystemLearn general approaches in data scienceExamine complete implementations that analyze large public datasetsDiscover which machine learning tools make sense for particular problemsExplore code that can be adapted to many uses.
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Seller: SecondSale, Montgomery, IL, U.S.A.
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Published by Oreilly & Associates Inc, 2017
ISBN 10: 1491972955 ISBN 13: 9781491972953
Language: English
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. 2nd edition. 330 pages. 9.00x7.00x0.50 inches. In Stock.
Published by Oreilly & Associates Inc, 2022
ISBN 10: 1098103653 ISBN 13: 9781098103651
Language: English
Seller: Revaluation Books, Exeter, United Kingdom
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Add to basketCondition: New. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark s Python API, and other best practices in Spark programming.Übe.
Paperback. Condition: New. The amount of data being generated today is staggering--and growing. Apache Spark has emerged as the de facto tool to analyze big data and is now a critical part of the data science toolbox. Updated for Spark 3.0, this practical guide brings together Spark, statistical methods, and real-world datasets to teach you how to approach analytics problems using PySpark, Spark's Python API, and other best practices in Spark programming.Data scientists Akash Tandon, Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills offer an introduction to the Spark ecosystem, then dive into patterns that apply common techniques--including classification, clustering, collaborative filtering, and anomaly detection--to fields such as genomics, security, and finance. This updated edition also covers NLP and image processing.If you have a basic understanding of machine learning and statistics and you program in Python, this book will get you started with large-scale data analysis.Familiarize yourself with Spark's programming model and ecosystemLearn general approaches in data scienceExamine complete implementations that analyze large public datasetsDiscover which machine learning tools make sense for particular problemsExplore code that can be adapted to many uses.
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Add to basketCondition: good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Good condition! Any other included accessories are also in Good condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear.