  Machine learning has rose to become an important research tool in the past
decade, its application has been expanded to almost if not all disciplines
known to mankind. Particularly, the use of machine learning in astrophysics
research had a humble beginning in the early 1980s, it has rose and become
widely used in many sub-fields today, driven by the vast availability of free
astronomical data online. In this short review, we narrow our discussion to a
single topic in astrophysics - the estimation of photometric redshifts of
galaxies and quasars, where we discuss its background, significance, and how
machine learning has been used to improve its estimation methods in the past 20
years. We also show examples of some recent machine learning photometric
redshift work done in Malaysia, affirming that machine learning is a viable and
easy way a developing nation can contribute towards general research in
astronomy and astrophysics.
