An introduction to core machine learning and nonparametric tools for economics, emphasizing prediction, classification, and causal inference. Students learn how to build, tune, and evaluate modern methods such as partitioned regression and splines, binned scatter plots, LASSO and ridge regression, decision trees, random forests, and neural networks. Time permitting the course covers embeddings, transformers, and attention. Computation using R is emphasized using real-world data.