  We apply machine learning algorithms to classify Infrared (IR)-selected
targets for NASA's upcoming SPHEREx mission. In particular, we are interested
in classifying Young Stellar Objects (YSOs), which are essential for
understanding the star formation process. Our approach differs from previous
work, which has relied heavily on broadband color criteria to classify
IR-bright objects, and are typically implemented in color-color and
color-magnitude diagrams. However, these methods do not state the confidence
associated with the classification and the results from these methods are quite
ambiguous due to the overlap of different source types in these diagrams. Here,
we utilize photometric colors and magnitudes from seven near and mid-infrared
bands simultaneously and employ machine and deep learning algorithms to carry
out probabilistic classification of YSOs, Asymptotic Giant Branch (AGB) stars,
Active Galactic Nuclei (AGN) and main-sequence (MS) stars. Our approach also
sub-classifies YSOs into Class I, II, III and flat spectrum YSOs, and AGB stars
into carbon-rich and oxygen-rich AGB stars. We apply our methods to
infrared-selected targets compiled in preparation for SPHEREx which are likely
to include YSOs and other classes of objects. Our classification indicates that
out of $8,308,384$ sources, $1,966,340$ have class prediction with probability
exceeding $90\%$, amongst which $\sim 1.7\%$ are YSOs, $\sim 58.2\%$ are AGB
stars, $\sim 40\%$ are (reddened) MS stars, and $\sim 0.1\%$ are AGN whose red
broadband colors mimic YSOs. We validate our classification using the spatial
distributions of predicted YSOs towards the Cygnus-X star-forming complex, as
well as AGB stars across the Galactic plane.
