  Machine Learning algorithms are good tools for both classification and
prediction purposes. These algorithms can further be used for scientific
discoveries from the enormous data being collected in our era. We present ways
of discovering and understanding astronomical phenomena by applying machine
learning algorithms to data collected with radio telescopes. We discuss the use
of supervised machine learning algorithms to predict the free parameters of
star formation histories and also better understand the relations between the
different input and output parameters. We made use of Deep Learning to capture
the non-linearity in the parameters. Our models are able to predict with low
error rates and give the advantage of predicting in real time once the model
has been trained. The other class of machine learning algorithms viz.
unsupervised learning can prove to be very useful in finding patterns in the
data. We explore how we use such unsupervised techniques on solar radio data to
identify patterns and variations, and also link such findings to theories,
which help to better understand the nature of the system being studied. We
highlight the challenges faced in terms of data size, availability, features,
processing ability and importantly, the interpretability of results. As our
ability to capture and store data increases, increased use of machine learning
to understand the underlying physics in the information captured seems
inevitable.
