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Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies by John D. Kelleher, Brian Mac Namee, Aoife D'Arcy

• Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies
  

• John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
  

• Page: 856
  

• Format: pdf, ePub, mobi, fb2
  

• ISBN: 9780262044691
  

• Publisher: MIT Press
  

Download Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies

Electronics books download free pdf Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning. The book is accessible, offering nontechnical explanations of the ideas underpinning each approach before introducing mathematical models and algorithms. It is focused and deep, providing students with detailed knowledge on core concepts, giving them a solid basis for exploring the field on their own. Both early chapters and later case studies illustrate how the process of learning predictive models fits into the broader business context. The two case studies describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book can be used as a textbook at the introductory level or as a reference for professionals.

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Fundamentals of Machine Learning for Predictive Data
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies (The Edition: Illustrated; Publisher: The MIT Press; Format: Hardcover (624 pages) quality of any book we rent, we'll immediately ship you another copy by 
Fundamentals of Machine Learning for Predictive Data
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies Data Analytics, second edition: Algorithms, Worked Examples.
Fundamentals of Machine Learning for Predictive Data
Fundamentals of Machine Learning for Predictive Data Analytics, Second Edition: Algorithms, Worked Examples, and Case Studies (Hardcover).
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Jan 9, 2021 - Amazon.com: Fundamentals of Machine Learning for Predictive Data Analytics, second edition: Algorithms, Worked Examples, and Case Studies 
Fundamentals of Machine Learning for Predictive Data
Algorithms, Worked Examples, and Case Studies John D. Kelleher, Brian Mac As described previously in this chapter , supervised machine learning is one of three chapters comes together in a successful predictive data analytics project .