ML Foundations for Product Leaders

You don’t need to build models to lead ML work — but you do need enough ML fluency to make better tradeoffs and guide teams toward shippable outcomes. This course covers the ML fundamentals that matter for product leaders: how models learn, where they fail, what data quality means, and how to evaluate performance in real-world settings. It adds decision frameworks for choosing approaches, collaborating across roles, and managing lifecycle risks like drift and monitoring. Best for PMs and product leaders integrating ML into their roadmap who want practical literacy to steer execution and communicate confidently with technical stakeholders.