Teaches the machine learning process for business students and professionals using automated machine learning, a new development in data science that requires only a few weeks to learn instead of years of trainingThough the concept of computers learning to solve a problem may still conjure thoughts of futuristic artificial intelligence, the reality is that machine learning algorithms now exist within most major software, including Websites and even word processors. These algorithms are transforming society in the most radical way since the Industrial Revolution, primarily through automating tasks such as deciding which users to advertise to, which machines are likely to break down, and which stock to buy and sell. While this work no longer always requires advanced technical expertise, it is crucial that practitioners and students alike understand the world of machine learning.
In this book, Kai R. Larsen and Daniel S. Becker teach the machine learning process using a new development in data science: automated machine learning (AutoML). AutoML, when implemented properly, makes machine learning accessible by removing the need for years of experience in the most arcane aspects of data science, such as math, statistics, and computer science. Larsen and Becker demonstrate how anyone trained in the use of AutoML can use it to test their ideas and support the quality of those ideas during presentations to management and stakeholder groups. Because the requisite investment is a few weeks rather than a few years of training, these tools will likely become a core component of undergraduate and graduate programs alike. With first-hand examples from the industry-leading DataRobot platform, Automated Machine Learning for Business provides a clear overview of the process and engages with essential tools for the future of data science.
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
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.
