Great machine learning is far more than picking a clever algorithm. The teams that succeed are the ones who frame the problem well, prepare honest data, and ship responsibly — and this course walks you through that whole journey with practical, example-driven steps.
Starting from a vague ask like “reduce churn”, you will define a real prediction target, hunt down the data problems that quietly wreck models, beat a simple baseline, and deploy something you can actually monitor over time. No heavy math — just the working judgment that separates a demo from a model people rely on.