  Next generation interferometers, such as the Square Kilometre Array, are set
to obtain vast quantities of information about the kinematics of cold gas in
galaxies. Given the volume of data produced by such facilities astronomers will
need fast, reliable, tools to informatively filter and classify incoming data
in real time. In this paper, we use machine learning techniques with a
hydrodynamical simulation training set to predict the kinematic behaviour of
cold gas in galaxies and test these models on both simulated and real
interferometric data. Using the power of a convolutional autoencoder we embed
kinematic features, unattainable by the human eye or standard tools, into a
three-dimensional space and discriminate between disturbed and regularly
rotating cold gas structures. Our simple binary classifier predicts the
circularity of noiseless, simulated, galaxies with a recall of $85\%$ and
performs as expected on observational CO and HI velocity maps, with a heuristic
accuracy of $95\%$. The model output exhibits predictable behaviour when
varying the level of noise added to the input data and we are able to explain
the roles of all dimensions of our mapped space. Our models also allow fast
predictions of input galaxies' position angles with a $1\sigma$ uncertainty
range of $\pm17^{\circ}$ to $\pm23^{\circ}$ (for galaxies with inclinations of
$82.5^{\circ}$ to $32.5^{\circ}$, respectively), which may be useful for
initial parameterisation in kinematic modelling samplers. Machine learning
models, such as the one outlined in this paper, may be adapted for SKA science
usage in the near future.
