Magnetic Resonance Fingerprinting (MRF) provides for simultaneous generation of MR multi-parametric maps from a single acquisition. In this work, a machine learning based regression method that does not require a dictionary has been demonstrated. A leave-one-out evaluation strategy was employed for numerical evaluation of the proposed MRF-RF approach. A comparative study was performed on two previously employed matching methods. Results depict that proposed MRF-RF method produces maps similar to the vector dot product approach, with a 10-fold saving in time. The method can also be extended to other non-linear maps such as B0 inhomogeneity, diffusion maps, and perfusion maps.