  Reservoir Computing has found many potential applications in the field of
complex dynamics. In this article, we exploit the exceptional capability of the
echo-state network (ESN) model to make it learn a unidirectional coupling
scheme from only a few time series data of the system. We show that, once
trained with a few example dynamics of a drive-response system, the machine is
able to predict the response system's dynamics for any driver signal with the
same coupling. Only a few time series data of an $A-B$ type drive-response
system in training is sufficient for the ESN to learn the coupling scheme.
After training even if we replace drive system $A$ with a different system $C$,
the ESN can reproduce the dynamics of response system $B$ using the dynamics of
new drive system $C$ only.
