  Exploiting the information provided by electron energy-loss spectroscopy
(EELS) requires reliable access to the low-loss region where the zero-loss peak
(ZLP) often overwhelms the contributions associated to inelastic scatterings
off the specimen. Here we deploy machine learning techniques developed in
particle physics to realise a model-independent, multidimensional determination
of the ZLP with a faithful uncertainty estimate. This novel method is then
applied to subtract the ZLP for EEL spectra acquired in flower-like WS$_2$
nanostructures characterised by a 2H/3R mixed polytypism. From the resulting
subtracted spectra we determine the nature and value of the bandgap of
polytypic WS$_2$, finding $E_{\rm BG} = 1.6_{-0.2}^{+0.3}\,{\rm eV}$ with a
clear preference for an indirect bandgap. Further, we demonstrate how this
method enables us to robustly identify excitonic transitions down to very small
energy losses. Our approach has been implemented and made available in an open
source Python package dubbed EELSfitter.
