  We propose a novel and fully data driven control scheme which relies on
machine learning (ML). Exploiting recently developed ML-based prediction
capabilities of complex systems, we demonstrate that nonlinear systems can be
forced to stay in arbitrary dynamical target states coming from any initial
state. We outline our approach using the examples of the Lorenz and the
R\"ossler system and show how these systems can very accurately be brought not
only to periodic but also to e.g. intermittent and different chaotic behavior.
Having this highly flexible control scheme with little demands on the amount of
required data on hand, we briefly discuss possible applications that range from
engineering to medicine.
