This course provides a brief introduction to the theory and practice of supervised machine learning, the discipline of teaching computers to make predictions from labeled data. We begin with a well-known model of linear regression, moving from fundamental principles to the advanced regularization techniques essential for building robust models. We then transition from regression to classification, exploring two major paradigms for separating data: discriminative models and generative models. The course concludes in learning how to critically evaluate and compare classifier performance using industry-standard tools such as the ROC Curve. Upon completion, you will have a strong command of the core principles that underpin modern predictive modeling.