  We assemble the largest CIV absorption line catalogue to date, leveraging
machine learning, specifically Gaussian processes, to remove the need for
visual inspection for detecting CIV absorbers. The catalogue contains
probabilities classifying the reliability of the absorption system within a
quasar spectrum. Our training set was a sub-sample of DR7 spectra that had no
detectable CIV absorption in a large visually inspected catalogue. We used
Bayesian model selection to decide between our continuum model and our
absorption-line models. Using a random hold-out sample of 1301 spectra from all
of the 26,030 investigated spectra in DR7 CIV catalogue, we validated our
pipeline and obtained an 87% classification performance score. We found good
purity and completeness values, both ~80%, when a probability of ~95% is used
as the threshold. Our pipeline obtained similar CIV redshifts and rest
equivalent widths to our training set. Applying our algorithm to 185,425
selected quasar spectra from SDSS DR12, we produce a catalogue of 113,775 CIV
doublets with at least 95% confidence. Our catalogue provides maximum a
posteriori values and credible intervals for CIV redshift, column density, and
Doppler velocity dispersion. We detect CIV absorption systems with a redshift
range of 1.37 $\!-\!$ 5.1, including 33 systems with a redshift larger than 5
and 549 absorbers systems with a rest equivalent width greater than 2 A at more
than 95% confidence. Our catalogue can be used to investigate the physical
properties of the circumgalactic and intergalactic media.
