This chapter discusses how deterministic data-driven approaches can be applied to the design of adaptive control systems for nonlinear plants affected by parametric, matched, unmatched, and functional uncertainties. After a brief outlook on the learning problem in general, this chapter presents three specific approaches, namely learn-then-control (LTC), sequential learn-and-control (SLC), and concurrent learn-and-control (CLC). Whereas numerical examples are provided for each of these three techniques, special emphasis is given to the theoretical aspects of LTC and CLC. Consistently with the rest of the book and to better emphasize the role of the results in Chap.  5 , this chapter exploits the properties of native spaces to create suitable approximations of the hypothesis space, and hence, of the functional uncertainties affecting the plant dynamics.