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Deploying Machine Learning, Paperback / softback Book

Deploying Machine Learning Paperback / softback

Description

Increasingly, business leaders and managers recognize that machine learning offers their companies immense opportunities for competitive advantage.

But most discussions of machine learning are intensely technical or academic, and don't offer practical information leaders can use to identify, evaluate, plan, or manage projects.

Deploying Machine Learning fills that gap, helping them clarify exactly how machine learning can help them, and collaborate with technologists to actually apply it successfully.

You'll learn: What machine learning is, how it compares to "big data" and "artificial intelligence," and why it's suddenly so importantWhat machine learning can do for you: solutions for computer vision, natural language processing, prediction, and moreHow to use machine learning to solve real business problems -- from reducing costs through improving decision-making and introducing new productsSeparating hype from reality: identifying pitfalls, limitations, and misconceptions upfrontKnowing enough about the technology to work effectively with your technical teamGetting the data right: sourcing, collection, governance, security, and cultureSolving harder problems: exploring deep learning and other advanced techniquesUnderstanding today's machine learning software and hardware ecosystemEvaluating potential projects, and addressing workforce concernsStaffing your project, acquiring the right tools, and building a workable project planInterpreting results -- and building an organization that can increasingly learn from dataUsing machine learning responsibly and ethicallyPreparing for tomorrow's advances The authors conclude with five chapter-length case studies: image, text, and video analysis, chatbots, and prediction applications.

For each, they don't just present results: they also illuminate the process the company undertook, and the pitfalls it overcame along the way.

Information

  • Format: Paperback / softback
  • Pages: 420 pages
  • Publisher: Pearson Education (US)
  • Publication Date:
  • Category: Data mining
  • ISBN: 9780135226209

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