This is an introductory course to explore how epidemiologists can use machine learning to advance their research. It consists of 6 modules; each one designed to include approximately 5 hours of material. The first module will provide a general introduction to machine learning and its utility for epidemiologists. The next two modules will introduce a variety of both unsupervised and supervised algorithms, as well as the validation techniques used in various contexts. The following three modules will then focus on specific applications of machine learning within the field of epidemiology, providing clear examples from the scientific literature. Each module will include self-assessments and hands-on programming exercises in R/R Studio to provide practical experience in the application of machine learning for epidemiologic research. Readings and examples will cover multiple substantive areas of epidemiology.