
Machine Learning for Business Analytics
Concepts, Techniques and Applications with JMP Pro
- English
- PDF
- Available on iOS & Android
Machine Learning for Business Analytics
Concepts, Techniques and Applications with JMP Pro
About this book
An up-to-date introduction to a market-leading platform for data analysis and machine learning
Machine Learning for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, 2nd 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, 2nd 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.
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Information
Table of contents
- Cover
- Title Page
- Copyright
- Contents
- Foreword
- Preface
- Acknowledgments
- PART I PRELIMINARIES
- PART II DATA EXPLORATION ANDDIMENSION REDUCTION
- PART III PERFORMANCE EVALUATION
- PART IV PREDICTION AND CLASSIFICATION METHODS
- PART V INTERVENTION AND USER FEEDBACK
- PART VI MINING RELATIONSHIPS AMONG RECORDS
- PART VII FORECASTING TIME SERIES
- PART VIII DATA ANALYTICS
- PART IX CASES
- References
- Data Files Used in the Book
- Index