Learn to Choose the Right ML Model

Learn to Choose the Right ML Model is an intermediate course for data scientists, ML engineers, and analytics-minded developers who want to make model choices you can defend—not just experiment and hope for the best. As machine learning powers more business-critical systems, success depends on moving beyond intuition and automating robust, fair, and metrics-driven selection and deployment. In this course, you’ll practice structured problem typing, compare major algorithm families, and apply real-world metrics to pick and monitor models that work in the wild. You'll learn through case studies (like Zillow, Apple Card, and Google Flu Trends), hands-on labs with Python and scikit-learn, and scenario-driven coaching. By the end, you’ll be able to frame ML problems, select and justify models, automate fairness and drift checks, and deploy pipelines you can trust—so your solutions succeed, not just on paper, but in production.