Machine learning is one of the most prominent application areas of quantum information science. Most of the proposed quantum machine learning tools have been offered so far are based discrete variable quantum computing model. However, generalization of the quantum machine learning tools to infinite dimension is important since some datasets have large variables that are not binary. To this end, continuous variable quantum computing (CVQC) model has been utilized and CV model of quantum machine learning have been proposed. In CVQC the information is encoded into quantum states of fields hence photonic hardware is a natural platform for realization of CVQC. This talk will review the recent advancements in CV quantum machine learning tools, and their implementability on the current quantum hardware.