Fundamentals of Machine Learning for Predictive Data Analytics

Fundamentals of Machine Learning for Predictive Data Analytics
Fundamentals of Machine Learning for Predictive Data Analytics
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Fundamentals of Machine Learning for Predictive Data Analytics
J. D. Kelleher
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Fundamentals of Machine Learning for Predictive Data Analytics

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Description

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.

Product Information
ISBN13 (SKU)
9780262044691
Title
Fundamentals of Machine Learning for Predictive Data Analytics
Author
J. D. Kelleher
Edition
2nd
Publisher
Penguin Random House SA
Publication Date
2021
Country of Publication
United States
Format Type
Physical
Number of Pages
856
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