P16 · ML Leadership & Product
scoping ML projects, make-vs-buy, leading DS teams
Machine learning for decision-makers: what models can and cannot promise, how to read an evaluation, and the questions a leader should ask before trusting one.
Most ML material teaches you to build models. This pillar teaches you to decide about them — to sit in the meeting where someone proposes a machine learning project and know which questions separate a sound plan from an expensive hope. It is written for leaders, managers and product owners: no code, no mathematics, no prerequisites, and every idea framed as something you can say out loud in a real conversation.
The single track walks a deliberate arc. First you learn what learning from examples actually is — and is not — so vendor promises have something to be measured against. Then trust: how ML projects create value and where they quietly fail. Then evaluation: what an accuracy number really says, and the metric questions that expose a shaky project. The arc closes with governance — ethics, honest data, and the responsibilities that stay with the decision-maker no matter who builds the model.