This article surveys geometric machine learning as an approach to identifying and exploiting structure in high-dimensional data. It discusses settings where Euclidean assumptions are insufficient, including non-Euclidean data such as graphs, strings, and matrices, as well as data governed by symmetries. The article reviews geometric methods for characterizing data structure and shows how understanding data geometry can lead to machine learning algorithms with stronger performance and provable guarantees.