  Asteroseismology is used to infer the interior physics of stars. The
\textit{Kepler} and TESS space missions have provided a vast data set of
red-giant light curves, which may be used for asteroseismic analysis. These
data sets are expected to significantly grow with future missions such as
\textit{PLATO}, and efficient methods are therefore required to analyze these
data rapidly. Here, we describe a machine learning algorithm that identifies
red giants from the raw oscillation spectra and captures \textit{p} and
\textit{mixed} mode parameters from the red-giant power spectra. We report
algorithmic inferences for large frequency separation ($\Delta \nu$), frequency
at maximum amplitude ($\nu_{max}$), and period separation ($\Delta \Pi$) for an
ensemble of stars. In addition, we have discovered $\sim$25 new probable red
giants among 151,000 \textit{Kepler} long-cadence stellar-oscillation spectra
analyzed by the method, among which four are binary candidates which appear to
possess red-giant counterparts. To validate the results of this method, we
selected $\sim$ 3,000 \textit{Kepler} stars, at various evolutionary stages
ranging from subgiants to red clumps, and compare inferences of $\Delta \nu$,
$\Delta \Pi$, and $\nu_{max}$ with estimates obtained using other techniques.
The power of the machine-learning algorithm lies in its speed: it is able to
accurately extract seismic parameters from 1,000 spectra in $\sim$5 seconds on
a modern computer (single core of the Intel Xeon Platinum 8280 CPU).
