Controlling nonlinear dynamical systems is a central task in many different areas of science and
engineering. Chaotic systems can be stabilized (or chaotified) with small perturbations, yet existing
approaches either require knowledge about the underlying system equations or large data sets as they
rely on phase space methods. In this work we propose a novel and fully data driven scheme relying on
machine learning (ML), which generalizes control techniques of chaotic systems without requiring a
mathematical model for its dynamics. Exploiting recently developed ML-based prediction capabilities,
we demonstrate that nonlinear systems can be forced to stay in arbitrary dynamical target states
coming from any initial state. We outline and validate our approach using the examples of the Lorenz
and the Rössler system and show how these systems can very accurately be brought not only to
periodic, but even to 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 ranging from engineering to medicine
