  The total mass estimate of molecular clouds suffers from the uncertainty in
the H$_2$-CO conversion factor, the so-called $X_{\rm CO}$ factor, which is
used to convert the $^{12}$CO (1--0) integrated intensity to the H$_2$ column
density. We demonstrate the machine learning's ability to predict the H$_2$
column density from the $^{12}$CO, $^{13}$CO, and C$^{18}$O (1--0) data set of
four star-forming molecular clouds; Orion A, Orion B, Aquila, and M17. When the
training is performed on a subset of each cloud, the overall distribution of
the predicted column density is consistent with that of the Herschel column
density. The total column density predicted and observed is consistent within
10\%, suggesting that the machine learning prediction provides a reasonable
total mass estimate of each cloud. However, the distribution of the column
density for values $> \sim 2 \times 10^{22}$ cm$^{-2}$, which corresponds to
the dense gas, could not be predicted well. This indicates that molecular line
observations tracing the dense gas are required for the training. We also found
a significant difference between the predicted and observed column density when
we created the model after training the data on different clouds. This
highlights the presence of different $X_{\rm CO}$ factors between the clouds,
and further training in various clouds is required to correct for these
variations. We also demonstrated that this method could predict the column
density toward the area not observed by Herschel if the molecular line and
column density maps are available for the small portion, and the molecular line
data are available for the larger areas.
