  The first data release of Apertif survey contains 3074 radio continuum images
covering a thousand square degrees of the sky. The observations were performed
during August 2019 to July 2020. The continuum images were produced at a
central frequency 1355 MHz with the bandwidth of $\sim$150 MHz and angular
resolution reaching 10". In this work we introduce and apply a new method to
obtain a primary beam model using a machine learning approach, Gaussian process
regression. The primary beam models obtained with this method are published
along with the data products for the first Apertif data release. We apply the
method to the continuum images, mosaic them and extract the source catalog. The
catalog contains 249672 radio sources many of which are detected for the first
time at these frequencies. We cross-match the coordinates with the NVSS,
LOFAR/DR1/value-added and LOFAR/DR2 catalogs resulting in 44523, 22825 and
152824 common sources respectively. The first sample provides a unique
opportunity to detect long term transient sources which have significantly
changed their flux density for the last 25 years. The second and the third ones
combined together provide information about spectral properties of the sources
as well as the redshift estimates.
