This short course helps you build and evaluate predictive models using supervised and unsupervised techniques. You will practice training algorithms with scikit-learn, explore how cross-validation affects model reliability, and analyze performance metrics like accuracy and F1 to make data-driven improvements. Instead of relying on guesswork, you’ll learn how to iterate systematically so your models meet defined performance targets. Through hands-on labs and guided coaching, you will build logistic-regression and clustering models, apply 5-fold cross-validation, and refine features until your model performs at the level you need. By the end, you will be able to apply these workflows to real predictive modeling tasks in retail and credit-risk contexts.