Since the launch of Galaxy Zoo (GZ) in 2009, the very first project on the Zooniverse citizen science platform, the number of data driven citizen science projects boosted. In the radio astronomy regime, the most well-known project is called Radio Galaxy Zoo. Initiated by the Evolutionary Map of the Universe (EMU) project team, RGZ 1 was launched at 2015 (Banfield et al. 2015) as a pathfinder for EMU data mining, so as an offshoot of GZ. Thanks to the help of over 12,000 RGZ citizen scientists, RGZ 1 team has published at least 14 papers, including discovery of some unusual objects and several machine learning algorithms. Recently, considering the success of RGZ 1, EMU and LOFAR team both decided to launch their own RGZ 2 projects, which should pave the way for additional tool and development work. In this seminar talk, I shall recap the motivation of RGZ1, the progress we made from the perspective of machine learning, and briefly introduce Radio Galaxy Zoo: EMU/LOFAR.