  Machine learning has emerged as a powerful tool in the field of gamma-ray
astrophysics. The algorithms can distinguish between different source types,
such as blazars and pulsars, and help uncover new insights into the high-energy
universe. The Large Area Telescope (LAT) on-board the Fermi Gamma-ray telescope
has significantly advanced our understanding of the Universe. The instrument
has detected a large number of gamma-ray emitting sources, among which a
significant number of objects have been identified as active galactic nuclei
(AGN). The sample is primarily composed of blazars; however, more than
one-third of these sources are either of an unknown class or lack a definite
association with a low-energy counterpart. In this work, we employ multiple
machine learning algorithms to classify the sources based on their other
physical properties. In particular, we utilized smart initialisation techniques
and self-supervised learning for classifying blazars into BL Lacertae objects
(BL Lac) and flat spectrum radio quasars (FSRQ). The core advantage of the
algorithm is its simplicity, usage of minimum number of features and easy
deployment due to lesser number of parameters without compromising on the
performance. The model predicts that out of the 1115 sources of uncertain type
in the 4FGL-DR3 catalog, 820 can be classified as BL Lacs, and 295 can be
classified as FSRQs.
