This course introduces the mathematical foundations and computational tools of modern machine learning for business and management. Students will build intuition for core supervised and unsupervised learning algorithms while gaining hands-on fluency with Python implementations on realistic data sets. Topics include regression and classification, decision trees and ensembles, clustering and dimensionality reduction, and neural networks for text and image analytics. Emphasis is placed on model evaluation, interpretability, and fairness. By the end of the course, students will be able to design, implement, and critically assess predictive models for complex business problems.