  We demonstrate identification of position, material, orientation and shape of
objects imaged by an $^{85}$Rb atomic magnetometer performing electromagnetic
induction imaging supported by machine learning. Machine learning maximizes the
information extracted from the images created by the magnetometer,
demonstrating the use of hidden data. Localization 2.6 times better than the
spatial resolution of the imaging system and successful classification up to
97$\%$ are obtained. This circumvents the need of solving the inverse problem,
and demonstrates the extension of machine learning to diffusive systems such as
low-frequency electrodynamics in media. Automated collection of task-relevant
information from quantum-based electromagnetic imaging will have a relevant
impact from biomedicine to security.
