This course demonstrates how to merge economic data analysis and applied econometric tools with the most common machine learning techniques, supporting the increasing need to understand “big data” due to the rapid advancement of computational methods. The course provides hands-on experience with the terminology, technology, and methodologies behind machine learning, with economic applications in marketing, finance, healthcare, and other areas. Primary topics include advanced regression techniques, resampling methods, model selection and regularization, classification models (logistic regression, Naive Bayes, discriminant analysis, k-nearest neighbors, neural networks), tree-based methods, support vector machines, and unsupervised learning (principal components analysis and clustering). Students apply both supervised and unsupervised machine learning techniques to solve economics-related problems with real-world datasets. No prior experience with R or Python is necessary.