Kai R Larsen, Daniel S Becker

Automated Machine Learning for Business

• Broschiertes Buch

Produktbeschreibung

This book teaches the full process of how to conduct machine learning in an organizational setting. It develops the problem-solving mind-set needed for machine learning and takes the reader through several exercises using an automated machine learning tool. To build experience with machine learning, the book provides access to the industry-leading AutoML tool, DataRobot, and provides several data sets designed to build deep hands-on knowledge of machine learning.

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Produktdetails

• Verlag: Sydney University Press
• Seitenzahl: 352
• Erscheinungstermin: 10. Juni 2021
• Englisch
• Abmessung: 251mm x 174mm x 24mm
• Gewicht: 586g
• ISBN-13: 9780190941666
• ISBN-10: 0190941669
• Artikelnr.: 61461854

Autorenporträt

Kai R. Larsen is an Associate Professor of Information Systems in the division of Organizational Leadership and Information Analytics, Leeds School of Business, University of Colorado Boulder. He is a courtesy faculty member in the Department of Information Science of the College of Media, Communication and Information, a Research Advisor to Gallup, and a Fellow of the Institute of Behavioral Science. Daniel S. Becker is a Data Scientist for Google's Kaggle division and founder of Kaggle Learn and Decision.ai.

Inhaltsangabe

Preface
Section I: Why Use Automated Machine Learning?
Chapter 1: What is Machine Learning?
Chapter 2: Automating Machine Learning
Section II: Defining Project Objectives
Chapter 3: Specify Business Problem
Chapter 4: Acquire Subject Matter Expertise
Chapter 5: Define Prediction Target
Chapter 6: Decide on Unit of Analysis
Chapter 7: Success, Risk, and Continuation
Section III: Acquire and Integrate Data
Chapter 8: Accessing and Storing Data
Chapter 9: Data Integration
Chapter 10: Data Transformations
Chapter 11: Summarization
Chapter 12: Data Reduction and Splitting
Section IV: Model Data
Chapter 13: Startup Processes
Chapter 14: Feature Understanding and Selection
Chapter 15: Build Candidate Models
Chapter 16: Understanding the Process
Chapter 17: Evaluate Model Performance
Chapter 18: Comparing Model Pairs
Chapter 19: Interpret Model
Chapter 20: Communicate Model Insights
Section VI: Implement, Document, and Maintain
Chapter 21: Set Up Prediction System
Chapter 22: Document Modeling Process for Reproducibility
Chapter 23: Create Model Monitoring and Maintenance Plan
Chapter 24: Seven Types of Target Leakage in Machine Learning and an Exercise
Chapter 25: Time-Aware Modeling
Chapter 26: Time-Series Modeling
References
Appendix A: Datasets
Appendix B: Optimization and Sorting Measures
Appendix C: More on Cross Variation