  We show that standard machine-learning algorithms may be trained to predict
certain invariants of algebraic number fields to high accuracy. A random-forest
classifier that is trained on finitely many Dedekind zeta coefficients is able
to distinguish between real quadratic fields with class number 1 and 2, to 0.96
precision. Furthermore, the classifier is able to extrapolate to fields with
discriminant outside the range of the training data. When trained on the
coefficients of defining polynomials for Galois extensions of degrees 2, 6, and
8, a logistic regression classifier can distinguish between Galois groups and
predict the ranks of unit groups with precision >0.97.
