Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
by John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
This comprehensive guide explores the fundamentals of machine learning for predictive data analytics, featuring essential algorithms, detailed worked examples, and insightful case studies. Authored by experts John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy, it equips readers with the knowledge to apply machine learning techniques effectively in data-driven environments.
About This Book
Fundamentals of Machine Learning for Predictive Data Analytics provides a comprehensive introduction to machine learning concepts specifically designed for predictive analytics applications. The book focuses on core algorithms that enable data-driven predictions and decision-making.
Authors John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy present the material through structured worked examples that illustrate key techniques in a clear, step-by-step manner. This approach helps readers grasp complex ideas without requiring advanced prerequisites.
The inclusion of case studies demonstrates real-world applications of machine learning in predictive data analytics, bridging theoretical knowledge with practical implementation. These examples highlight how algorithms are used to analyze data and generate actionable insights.
Overall, the book serves as an essential resource for learners seeking to understand the building blocks of machine learning in the context of predictive analytics, emphasizing both foundational principles and hands-on practice.
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