Machine Learning for Business Analytics

Peter C. Bruce , Mia L. Stephens , Galit Shmueli , Muralidhara Anandamurthy , Nitin R. Patel 지음

Wiley 2023년 04월 18일 출간

작품소개

책소개

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.

목차

Foreword xix Preface xx Acknowledgments xxiii PART I PRELIMINARIES 1 Introduction 3 1.1 What Is Business Analytics? 3 1.2 What Is Machine Learning? 5 1.3 Machine Learning, AI, and Related Terms 5 Statistical Modeling vs. Machine Learning 6 1.4 Big Data 6 1.5 Data Science 7 1.6 Why Are There So Many Different Methods? 8 1.7 Terminology and Notation 8 1.8 Road Maps to This Book 10 Order of Topics 12 2 Overview of the Machine Learning Process 17 2.1 Introduction 17 2.2 Core Ideas in Machine Learning 18 Classification 18 Prediction 18 Association Rules and Recommendation Systems 18 Predictive Analytics 19 Data Reduction and Dimension Reduction 19 Data Exploration and Visualization 19 Supervised and Unsupervised Learning 19 2.3 The Steps in A Machine Learning Project 21 2.4 Preliminary Steps 22 Organization of Data 22 Sampling from a Database 22 Oversampling Rare Events in Classification Tasks 23 Preprocessing and Cleaning the Data 23 2.5 Predictive Power and Overfitting 29 Overfitting 29 Creation and Use of Data Partitions 31 2.6 Building a Predictive Model with JMP Pro 34 Predicting Home Values in a Boston Neighborhood 34 Modeling Process 36 2.7 Using JMP Pro for Machine Learning 42 2.8 Automating Machine Learning Solutions 43 Predicting Power Generator Failure 44 Uber’s Michelangelo 45 2.9 Ethical Practice in Machine Learning 47 Machine Learning Software: The State of the Market by Herb Edelstein 47 Problems 52 PART II DATA EXPLORATION AND DIMENSION REDUCTION 3 Data Visualization 59 3.1 Introduction 59 3.2 Data Examples 61 Example 1: Boston Housing Data 61 Example 2: Ridership on Amtrak Trains

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작가정보

저자(글) Peter C. Bruce

저자(글) Mia L. Stephens

저자(글) Galit Shmueli

저자(글) Muralidhara Anandamurthy

저자(글) Nitin R. Patel