Selecting and Formulating Problems for Machine Learning

Not every business problem should be solved with machine learning. Knowing how to identify, evaluate, and scope the right problems is the most critical first step in any AI initiative. This text-only course guides you through the foundational concepts of machine learning problem selection, helping you distinguish viable projects from those better suited for traditional software. What you will learn: • Understand key terminology and the core criteria that make a problem suitable for machine learning. • Assess data availability, quality, and volume requirements before launching a project. • Formulate business challenges into concrete machine learning tasks such as classification or regression. • Evaluate modern alternatives, including when to use predictive machine learning versus generative AI models. • Analyze project risks, ethical considerations, and potential biases during the scoping phase. • Practice your skills with written scenario-based self-assessments. Starting with fundamental definitions of machine learning viability, the course guides you through structured evaluation frameworks and practical scoping exercises. Designed for beginners, product managers, and business analysts, this course requires no prior coding or mathematical background. Start reading today to master the art of selecting successful machine learning projects.