Shmueli Galit et al. Machine Learning for Business Analytics: Concepts, Techniques and Applications with JMP Pro

2nd Edition. — Galit Shmueli, Mia L. Stephens, Muralidhara Anandamurthy, Nitin R. Patel, Peter C. Bruce. — Wiley, 2023. — 611 p. — ISBN: 978-1119903833.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.PART I PRELIMINARIESOverview of the Machine Learning Process.
PART II DATA EXPLORATION AND DIMENSION REDUCTION
Data Visualization.
Dimension Reduction.
PART III PERFORMANCE EVALUATION
Evaluating Predictive Performance.
PART IV PREDICTION AND CLASSIFICATION METHODS
Multiple Linear Regression.
k-Nearest Neighbors (k-NN).
The Naive Bayes Classifier.
Classification and Regression Trees.
Logistic Regression.
Neural Nets.
Discriminant Analysis.
Generating, Comparing, and Combining Multiple Models.
PART V INTERVENTION AND USER FEEDBACK
Interventions: Experiments, Uplift Models, and Reinforcement Learning.
PART VI MINING RELATIONSHIPS AMONG RECORDS
Association Rules and Collaborative Filtering.
Cluster Analysis.
PART VII FORECASTING TIME SERIES
Handling Time Series.
Regression-Based Forecasting.
Smoothing and Deep Learning Methods for Forecasting.
PART VIII DATA ANALYTICS
Text Mining.
Responsible Data Science.
PART IX CASES
Cases.