Establishing the host range for novel viruses remains a challenge. We address the challenge of identifying non-human animal coronaviruses that may infect humans by creating a machine learning model that learns from the binding of the spike protein of alpha and beta coronaviruses to their host receptor. The proposed method produces a Human- Binding Potential (h-BiP) score that distinguishes, with high accuracy, binding potential among human coronaviruses. We further analyze the binding properties of viruses of interest using molecular dynamics. To test whether the model can be used for surveillance of novel coronaviruses, we re-trained the model on a set that excludes SARS-COV-2 viral sequences. The results predict the binding of SARS-CoV-2 with a human-receptor indicating that machine learning methods provide an excellent tool for the prediction of host expansion events.