Description
Fundamentals of Machine Learning for Predictive Data Analytics is a textbook that offers a detailed introduction to machine learning approaches used in predictive data analytics. The book covers both theoretical concepts and practical applications, making it suitable for students and professionals alike. It includes explanatory worked examples and case studies that illustrate the application of machine learning models in business contexts.
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The second edition of this book introduces recent developments in machine learning, including a new chapter on deep learning and additional chapters on unsupervised learning and reinforcement learning. The book is designed to be accessible, providing nontechnical explanations of the ideas underpinning each approach before delving into mathematical models and algorithms.
Authors John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy bring their expertise to the book, offering insights into the field of machine learning. John D. Kelleher is an academic leader at Technological University Dublin, Brian Mac Namee is an associate professor at University College Dublin, and Aoife D'Arcy is the CEO of Krisolis, a data analytics company.
The book is available in hardcover and eBook formats, published by MIT Press. It can be used as a textbook at the introductory level or as a reference for professionals looking to deepen their understanding of predictive data analytics.
- CategoryForecasting & Predictive Analytics
Book Details
Comprehensive introduction to machine learning
Focus on predictive data analytics
Includes worked examples and case studies
Covers recent developments in machine learning
New chapters on deep learning, unsupervised learning, and reinforcement learning
Accessible explanations of complex concepts
Suitable for students and professionals
Authored by experts in the field
Who This Book Is For
- Price prediction
- Risk assessment
- Customer behavior prediction
- Document classification
- Business problem formulation
