  We report a direct scheme calculation of kinetic energy functional derivative
using Machine Learning. Support Vector Regression and Kernel Ridge Regression
techniques were independently employed to estimate the kinetic energy
functional and its derivative. Even though the accuracy should have been a
decisive factor in modeling a realistic functional, we show that at a certain
level it affects the generalizability of the model. By choosing the right
regularization term and by considering a reasonable interplay between it and
the accuracy, we were able to deduce the functional derivative from a model
that was trained to estimate the kinetic energy. Although the derivative
calculations demand very high accuracy to account for small variations of the
kinetic energy, the developed estimator was capable of capturing these
extremely small changes of the electron density. This work pours into highly
effective implementation of the orbital-free density functional theory as it
employs only direct calculation scheme
