Machine Learning for Business Analytics Concepts, Techniques and Applications with JMP Pro, 2nd Edition byGalit Shmueli, Peter Bruce, Mia Stephens, Muralidhara Anandamurthy, Nitin Patel 9781119903833 1119903831

Machine Learning for Business Analytics: Concepts, Techniques and Applications with JMP Pro, 2nd Edition Galit Shmueli – Ebook Instant Download/Delivery ISBN(s): 9781119903833, 1119903831

Product details:

• ISBN 10: 1119903831
• ISBN 13: 9781119903833
• Author: Galit, Peter, Mia, Muralidhara, Nitin

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro^®, 2^nd ed. offers an accessible and engaging introduction to machine learning. It provides concrete examples and case studies to educate new users and deepen existing users’ understanding of their data and their business. Fully updated to incorporate new topics and instructional material, this remains the only comprehensive introduction to this crucial set of analytical tools specifically tailored to the needs of businesses.

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro^®, 2^nd ed. readers will also find:

• Updated material which improves the book’s usefulness as a reference for professionals beyond the classroom
• Four new chapters, covering topics including Text Mining and Responsible Data Science
• An updated companion website with data sets and other instructor resources: www.jmp.com/dataminingbook
• A guide to JMP Pro^®’s new features and enhanced functionality

Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro^®, 2nd ed. is ideal for students and instructors of business analytics and data mining classes, as well as data science practitioners and professionals in data-driven industries.

Table Contents:

1. PART I: PRELIMINARIES
2. PART II: DATA EXPLORATION AND DIMENSION REDUCTION
3. PART III: PERFORMANCE EVALUATION
4. PART IV: PREDICTION AND CLASSIFICATION METHODS
5. PART V: INTERVENTION AND USER FEEDBACK
6. PART VI: MINING RELATIONSHIPS AMONG RECORDS
7. PART VII: FORECASTING TIME SERIES
8. PART VIII: DATA ANALYTICS
9. PART IX: CASES

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