We present a new approach to quantum process tomography, the reconstruction of an unknown quantum channel from measurement data. Specifically, we combine a tensor-network representation of the Choi matrix (a complete description of a quantum channel), with unsupervised machine learning of single-shot projective measurement data. We show numerical experiments for both unitary and noisy quantum circuits, for a number of qubits well beyond the reach of standard process tomography techniques.