Duke University: AI product management specialization

Duke University’s AI Product Management Specialization on Coursera is a three-course program taught by Jon Reifschneider from Duke’s Pratt School of Engineering. With more than 60,000 learners enrolled and a 4.7/5 rating across over 1,100 reviews, it is one of the most established AI PM programs on the platform.

The specialization is designed for product professionals who want to understand how machine learning works well enough to manage it — without needing to write code or build models themselves. No prior programming experience is required, and the content is structured for beginners.

Curriculum

The program runs across three sequential courses, requiring roughly five hours per week over four months.

Machine learning foundations for product managers (16 hours) introduces ML algorithms, model evaluation methods, and hands-on model training using accessible tools. The goal is conceptual understanding, not technical implementation. By the end, a PM should be able to distinguish between types of ML problems and evaluate whether a given approach fits a product situation.

Managing machine learning projects (18 hours) covers the full data science process: designing ML systems, preparing data, managing iterative development, and monitoring deployed models in production. This course is the most directly relevant for PMs who coordinate between engineering and business stakeholders on AI projects.

Human factors in AI (18 hours) addresses ethics, privacy considerations, and user experience design for AI-powered products. It examines how to design AI features that users can understand and trust, and how to navigate organizational decisions involving fairness and accountability.

Who it is for

Product managers who work alongside ML or data science teams and want to close the vocabulary and conceptual gap with engineers. Also useful for PMs who evaluate whether to propose AI solutions for specific product problems, and for those who need to explain AI trade-offs to non-technical stakeholders.

What it does not cover

The specialization does not address generative AI or large language models in depth. Its ML foundations course was designed before these became central to product work. PMs building products on LLM infrastructure should treat this as a foundation and supplement it with more recent material on prompt engineering and retrieval-augmented systems.

Enrollment requires a Coursera subscription; financial aid is available for those who qualify.