Fundamentals of machine learning for predictive data analytics pdf download

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Embrace machine learning approaches and Python to enable automatic rendering of rich insights and solve business problem. This book gathers papers addressing state-of-the-art research in the areas of machine learning and predictive analysis,. This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are ex. Until recently, many people thought big data was a passing fad.

Today, b. A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popul. Concepts of Machine Learning with Practical Approaches.

You can publish your own PDF file online for free in a few minutes! Sign Up. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. 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 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. More documents Similar magazines Info. Share from cover. Share from page:. Flag as Inappropriate Cancel.

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