Low frequency earthquakes (LFEs) originating below the central San Andreas Fault are associated with slow-slip within the more ductile portion of the crust beneath the seismogenic zone. Monitoring efforts over 15 years recorded >1 million LFEs with >70 per day. We apply machine learning (ML) to statistical features describing the seismic waveforms and estimate the LFE daily intensity. Using 4 years of independent data, the ML model produces a 0.68 correlation. The burst-like LFE behavior is reproduced and the largest misfit occurs during the low-amplitude daily undulations. The ability to continuously monitor LFE activity provides insight to when geodetic measurements of slow slip are possible, without the need for developing a computational-intensive template-matching catalog. Similarities are found between detecting LFEs and tremors, which provides evidence tremors are composed of LFEs. The approach reveals by ML the rich information contained in the features of continuous seismic waveforms.