Predictive analytics with AI starts with a practical question: where AI helps, and where it can quietly distort the result. This course shows how to turn business prediction problems into decision problems, choose and compare models, and judge them with evaluation that reflects real consequences. It is aimed at analysts, data scientists, and business professionals who want a grounded way to use AI for forecasting and classification without skipping the hard parts. By the end, the course connects model choice, measurement, and deployment concerns into one usable workflow.
The course covers where AI fits in predictive analytics, how to frame predictions as decisions, and how to let AI assist without leakage. It also covers model comparison from baselines to boosted trees, evaluation tied to business consequences, and production readiness with monitoring and next steps.