文件名称：Fundamentals.of.Machine.Learning.for.Predictive.Data.Analytics.02620294
文件大小：14.29MB
文件格式：PDF
更新时间：2019-01-18 07:46:56
Machine Learning Predictive Data
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. 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. Table of Contents Chapter 1 Machine Learning for Predictive Data Analytics Chapter 2 Data to Insights to Decisions Chapter 3 Data Exploration Chapter 4 Information-based Learning Chapter 5 Similarity-based Learning Chapter 6 Probability-based Learning Chapter 7 Error-based Learning Chapter 8 Evaluation Chapter 9 Case Study: Customer Churn Chapter 10 Case Study: Galaxy Classification Chapter 11 The Art of Machine Learning for Predictive Data Analytics Appendix A Descriptive Statistics and Data Visualization for Machine Learning Appendix B Introduction to Probability for Machine Learning Appendix C Differentiation Techniques for Machine Learning