  We describe how simple machine learning methods successfully predict
geometric properties from Hilbert series (HS). Regressors predict embedding
weights in projective space to ${\sim}1$ mean absolute error, whilst
classifiers predict dimension and Gorenstein index to $>90\%$ accuracy with
${\sim}0.5\%$ standard error. Binary random forest classifiers managed to
distinguish whether the underlying HS describes a complete intersection with
high accuracies exceeding $95\%$. Neural networks (NNs) exhibited success
identifying HS from a Gorenstein ring to the same order of accuracy, whilst
generation of 'fake' HS proved trivial for NNs to distinguish from those
associated to the three-dimensional Fano varieties considered.
