  The muon tagging is an essential tool to distinguish between gamma and
hadron-induced showers in wide field-of-view gamma-ray experiments. In this
work, it is shown that a good muon tagging (and counting) can be achieved using
a water Cherenkov detector with a reduced water volume and 4 PMTs, provided
that the PMT signal spatial and time patterns are interpreted by an analysis
based on Machine Learning. The developed analysis has been tested for different
shower and array configurations. The output of the ML analysis, the probability
of having a muon in the WCD station, has been used to notably discriminate
between gamma and hadron induced showers with $S/ \sqrt{B} \sim 4$ for shower
with energies $E_0 \sim1\,$TeV.
