Applied Machine Learning Problem Solving Questions

Describes a systematic approach to solving applied machine learning problems from ambiguous product or business goals. Topics include problem scoping and success metrics, building and evaluating simple baselines, data exploration and feature engineering, model selection and validation, offline and online evaluation strategies, iteration cycles and when to escalate to more complex modeling, and connecting technical improvements to business outcomes.