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    Machine Learning for Hackers (Paperback) By (author) Drew Conway, By (author) John Myles White

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    DescriptionNow that storage and collection technologies are cheaper and more precise, methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to crunch data. With this book, you'll learn machine learning and statistics tools in a practical fashion, using black-box solutions and case studies instead of a traditional math-heavy presentation. By exploring each problem in this book in depth - including both viable and hopeless approaches - you'll learn to recognize when your situation closely matches traditional problems. Then you'll discover how to apply classical statistics tools to your problem. Machine Learning for Hackers is ideal for programmers from private, public, and academic sectors.

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    Machine Learning for Hackers
    Authors and contributors
    By (author) Drew Conway, By (author) John Myles White
    Physical properties
    Format: Paperback
    Number of pages: 322
    Width: 178 mm
    Height: 233 mm
    Thickness: 19 mm
    Weight: 517 g
    ISBN 13: 9781449303716
    ISBN 10: 1449303714

    BIC E4L: COM
    Nielsen BookScan Product Class 3: S10.2
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 01
    B&T Modifier: Subject Development: 20
    B&T Modifier: Academic Level: 05
    B&T General Subject: 229
    B&T Modifier: Text Format: 01
    LC subject heading:
    DC22: 005.1
    LC subject heading:
    B&T Merchandise Category: COM
    B&T Approval Code: A93600300
    Ingram Subject Code: XL
    Warengruppen-Systematik des deutschen Buchhandels: 16360
    BISAC V2.8: COM051300
    BIC subject category V2: UYQM
    BISAC V2.8: COM042000
    B&T Approval Code: A93203642
    BISAC V2.8: COM037000
    DC22: 004.01/5113
    DC23: 006.31
    B&T Approval Code: A51823000
    LC classification: QA76.9.A43 C65 2012
    LC subject heading: , ,
    O'Reilly Media, Inc, USA
    Imprint name
    O'Reilly Media, Inc, USA
    Publication date
    01 April 2012
    Publication City/Country
    Author Information
    Drew Conway is a PhD candidate in Politics at NYU. He studies international relations, conflict, and terrorism using the tools of mathematics, statistics, and computer science in an attempt to gain a deeper understanding of these phenomena. His academic curiosity is informed by his years as an analyst in the U.S. intelligence and defense communities. John Myles White is a PhD candidate in Psychology at Princeton. He studies pattern recognition, decision-making, and economic behavior using behavioral methods and fMRI. He is particularly interested in anomalies of value assessment.