This paper deals with the problem of estimating the derivatives of a regression function based on biased data. We develop two different linear wavelet estimators according to the knowledge of the "biased density" of the design. The new estimators are analyzed with respect to their $L_p$ risk with p ≥ 1 over Besov balls. Fast polynomial rates of convergence are obtained.