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Fundamentals.of.Machine.Learning.for.Predictive.Data.Analytics.02620294 评分:

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 treatme nt 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
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2015-12-29 上传大小:14.29MB
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评论 共17条

dawnia 这个是epub转换版,不是原版PDF。很多表格都乱掉了,而且图片、公式不清晰。
2018-02-02
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limint86 好书,谢谢分享
2018-01-16
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commanager 好处,很好,谢谢分享
2018-01-11
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lliufeng987 很好的书,很好
2017-10-15
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q4quebec 很好的书,感谢分享
2017-10-05
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wangjian052163 好处,很好,谢谢分享
2017-09-26
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kgfguo 文字版,带目录,很清晰
2017-09-12
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dingweilong 一本经典的书
2017-02-23
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ccc8848 真的很不错,谢谢!
2016-12-25
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