Acceleration of first order methods is mainly obtained via inertial techniques à la Nesterov, or via nonlinear extrapolation. The latter has known a recent surge of interest, with successful applications to gradient and proximal gradient techniques. On multiple Machine Learning problems, coordinate descent achieves performance significantly superior to full-gradient methods. Speeding up coordinate descent in practice is not easy: inertially accelerated versions of coordinate descent are theoretically accelerated, but may not always lead to practical speed-ups. We propose an accelerated version of coordinate descent using extrapolation, which shows considerable speed up in practice, compared to both inertial accelerated coordinate descent and extrapolated proximal gradient descent. Experiments on Least Squares, Lasso, elastic net, group Lasso, and Logistic Regression validate the approach.