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

| |**Book Name**: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

**Author**: John D. Kelleher, Brian Mac Namee, Aoife D’Arcy

**Publisher**: The MIT Press

**ISBN-10**: 978-0262029445, 0262029448

**Year**: 2015

**Pages**: 691

**Language**: English

**File size**: 15 MB

**File format**: PDF

### Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies Pdf Book Description:

These versions can be found in predictive information analytics software such as cost forecast, hazard assessment, forecasting customer behaviour, and document classification. This introductory textbook provides a comprehensive and focused treatment of the most significant machine learning procedures used in predictive analytics, covering both the theoretical concepts and practical applications. Technical and mathematical material has been reinforced with self explanatory worked examples, and case studies demonstrate the use of those models in the wider business context.After talking the trajectory from information to insight into conclusion, the publication describes four methods to machine learning: information-based learning, and similarity-based learning, probability-based learning, and error-based learning. Each one of the approaches is introduced with a nontechnical explanation of the inherent theory, followed by mathematical models and calculations exemplified by detailed worked examples. In the end, the book considers techniques for assessing prediction models and provides two case studies which explain particular data analytics jobs through every stage of development, from inventing the company issue to implementation of this analytics solution. The publication, informed by the authors’ years of instructing system learning, and focusing on predictive information analytics jobs, is Acceptable for use by undergraduates in computer science, technology, math, or data; by grad students in areas with software for predictive information analytics; and as a benchmark for professionals

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