We have developed methods for machine-learning the electric response properties of chemical and material systems (e.g. polarizabilities) that avoid expensive quantum-chemical calculations, generalizing existing methods for learning properties such as energies and forces and paving the way to efficient and accurate simulation of vibrational spectroscopy.