  We present a methodology based on the implementation of a fully connected
neural network to estimate the gravitational wave (GW) temporal evolution of
the gmode fundamental resonant frequency for a Core Collapse Supernova (CCSN).
To perform the estimation, we construct a training data set, using synthetic
waveforms, that serves to train the ML algorithm, and then use several CCSN
waveforms to test the model. According to the results obtained from the
implementation of our model, we provide numerical evidence to support the
classification of progenitors according to their degree of rotation. The
relative error associated with the estimate of the slope of the resonant
frequency versus time for the GW from CCSN signals is within $13\%$ for the
tested candidates included in this study. This method of classification does
not require priors or templates, it is based on physical modelling, and can be
combined with studies that classify the progenitor with other physical
features.
