Machine Learning for Business Analytics: Concepts, Techniques and Applications with JMP Pro, Second Edition
- 10h 40m
- Galit Shmueli, Mia L. Stephens, Muralidhara Anandamurthy, Nitin R. Patel, Peter C. Bruce
- John Wiley & Sons (US)
- 2023
MACHINE LEARNING FOR BUSINESS ANALYTICS
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.
About the Author
Galit Shmueli, PhD is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed business analytics courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan.
Peter C. Bruce is Founder of the Institute for Statistics Education at Statistics.com, and Chief Learning Officer at Elder Research, Inc.
Mia L. Stephens, M.S. is an Advisory Product Manager with JMP, driving the product vision and roadmaps for JMP® and JMP Pro®.
Muralidhara Anandamurthy, PhD is an Academic Ambassador with JMP, overseeing technical support for academic users of JMP Pro®.
Nitin R. Patel, PhD is cofounder and lead researcher at Cytel Inc. He is also a Fellow of the American Statistical Association and has served as a visiting professor at the Massachusetts Institute of Technology and Harvard University, among others.
In this Book
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Introduction
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Overview of the Machine Learning Process
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Data Visualization
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Dimension Reduction
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Evaluating Predictive Performance
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Multiple Linear Regression
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k‐Nearest Neighbors (k‐NN)
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The Naive Bayes Classifier
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Classification and Regression Trees
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Logistic Regression
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Neural Nets
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Discriminant Analysis
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Generating, Comparing, and Combining Multiple Models
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Interventions—Experiments, Uplift Models, and Reinforcement Learning
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Association Rules and Collaborative Filtering
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Cluster Analysis
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Handling Time Series
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Regression‐Based Forecasting
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Smoothing and Deep Learning Methods for Forecasting
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Text Mining
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Responsible Data Science
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Cases
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References
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Data Files Used in the Book
