Machine Learning for Hackers

Machine Learning for Hackers

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Now 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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  • Paperback | 322 pages
  • 175.26 x 231.14 x 20.32mm | 544.31g
  • O'Reilly Media, Inc, USA
  • SebastopolUnited States
  • English
  • 1449303714
  • 9781449303716
  • 172,246

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About Drew Conway

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.

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