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.
We found the case studies in Chapters 9 and 10 to be the most useful, on customer churn and galaxy classification. As always the challenge is to ensure you ask yourself the right questions before hand about what type of data you are hoping to collect, and how you plan to gather this. If you are too prescriptive and narrow about the parameters and types of data collected you will limit your ability to achieve unexpected insights. For those in the data analysis area, and those hoping to move in it, this is a useful book, and the upcoming course in May could also be of real help too.
— The Analytics Store (@analyticsStore) March 22, 2016
More about the book here.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution.
— The Analytics Store (@analyticsStore) April 4, 2016
The book, informed by the authors’ many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
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John D. Kelleher is a Lecturer at the Dublin Institute of Technology, and a founding member of DIT’s Applied Intelligence Research Center.
Brian Mac Namee is a Lecturer at University College Dublin.
Aoife D’Arcy is CEO of The Analytics Store, a data analytics consultancy and training company.