This course is designed to expose students to the complex real-world datasets commonly used in machine learning applications. The course provides an accessible introduction to supervised machine learning, while covering aspects of data collection and cleaning. Specific topics include model construction, evaluation, and regularization, as well as web scraping, text data, feature construction, and measurement error. Students complete short assignments, longer homework sets, and a final project.