Automated Machine Learning for Business

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.

PrefaceSection I: Why Use Automated Machine Learning?Chapter 1: What is Machine Learning?Chapter 2: Automating Machine LearningSection II: Defining Project ObjectivesChapter 3: Specify Business ProblemChapter 4: Acquire Subject Matter ExpertiseChapter 5: Define Prediction TargetChapter 6: Decide on Unit of AnalysisChapter 7: Success, Risk, and ContinuationSection III: Acquire and Integrate DataChapter 8: Accessing and Storing DataChapter 9: Data IntegrationChapter 10: Data TransformationsChapter 11: SummarizationChapter 12: Data Reduction and SplittingSection IV: Model DataChapter 13: Startup ProcessesChapter 14: Feature Understanding and SelectionChapter 15: Build Candidate ModelsChapter 16: Understanding the ProcessChapter 17: Evaluate Model PerformanceChapter 18: Comparing Model PairsChapter 19: Interpret ModelChapter 20: Communicate Model InsightsSection VI: Implement, Document, and MaintainChapter 21: Set Up Prediction SystemChapter 22: Document Modeling Process for ReproducibilityChapter 23: Create Model Monitoring and Maintenance PlanChapter 24: Seven Types of Target Leakage in Machine Learning and an ExerciseChapter 25: Time-Aware ModelingChapter 26: Time-Series ModelingReferencesAppendix A: DatasetsAppendix B: Optimization and Sorting MeasuresAppendix C: More on Cross Variation