Building on data science concepts, this course examines how supervised and unsupervised machine learning methods are applied as decision-support tools in business contexts. Students gain hands-on experience implementing models while focusing on model selection, evaluation, business relevance, and deployment considerations, rather than purely technical optimization.
Applications include forecasting, customer behavior analysis, risk modeling, and operational improvement, with strong emphasis on translating model outputs into actionable business decisions. The course highlights practical constraints such as data availability, bias, interpretability, and organizational adoption, ensuring students can responsibly apply machine learning within real business environments.