Most of the AI education world is built for the person who will train the model, and almost none of it is built for the person who has to decide whether the model should be built at all, scope it, staff it, and ship it to users who do not care how it works. Duke's specialization is one of the few that takes that second person seriously. Across three courses it covers machine learning foundations explained for non-engineers, the management of machine learning projects, and the human factors of designing AI products, and the through line is that an AI project is mostly a management problem dressed up as a technical one. The foundations course gives you enough conceptual grip on how machine learning behaves to ask sharp questions in a planning meeting without pretending to make you a practitioner, which is exactly the right level for the audience.
The project management material is the strongest part, because it confronts the thing that actually sinks AI products, which is that you are managing genuine uncertainty, you do not know up front whether the data will support the thing the business wants, and the course treats that as the central challenge rather than an afterthought. The human factors course earns its place too, since plenty of technically sound models fail because nobody thought about how a person would trust or use them. My caveats are simply the flip side of its strengths. It is deliberately light on technical depth, so an engineer looking to actually build will find it too conceptual and should go elsewhere, and you finish it equipped to lead the work rather than to do it.
Some of the case studies also date quickly given how fast the field moves. But judged against what it sets out to do, equip a product person to make good calls about AI, it is one of the clearest and most useful things on Coursera, and I would recommend it to any PM or founder staring down their first serious AI feature.