Applied Machine Learning

Description from DukeHub

Applied introduction to prediction and decision-making based on data using machine learning techniques. This course covers structuring machine learning problems, selecting appropriate algorithms and understanding their operation, evaluating model performance using appropriate metrics, and interpreting model outputs. This course provides hands-on practice applying machine learning tools while developing conceptual understanding of the methods, workflows, and best practices for effective and responsible application

Skills

• ai and machine learningdescription: "machine learning" and 2 more
• data and statisticsAI review: "evaluating model performance using appropriate metrics"

AI-resilient: 2 of 5 2/5

• ✓Yes: Physical, lab or field work · "hands-on"
• No: Live work with people
• ✓Yes: Judgment someone is accountable for · "decision"
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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