Application of machine learning to chemical and physical problems is a growing field with applications ranging from discovery of new molecules and materials to improving existing theoretical models. The present article is about using machine learning to represent potential energy surfaces for small molecules and chemical reactions. The methods discussed in this article can be used for representing sets of coupled potential energy surfaces as required for simulating electronically nonadiabatic photochemical processes.