diff --git a/examples/plot_kernel_approximation.py b/examples/plot_kernel_approximation.py
index 4aca910f227e5ae1695540afa76f99dd07351f8d..cbf78be4dfbc0368788a16bd72be2fbe3e0d9131 100644
--- a/examples/plot_kernel_approximation.py
+++ b/examples/plot_kernel_approximation.py
@@ -17,7 +17,7 @@ of :class:`RBFSampler`, which uses random Fourier features) and different sized
 subsets of the training set (for :class:`Nystroem`) for the approximate mapping
 are shown.
 
-Please not that the dataset here is not large enough to show the benefits
+Please note that the dataset here is not large enough to show the benefits
 of kernel approximation, as the exact SVM is still reasonably fast.
 
 Sampling more dimensions clearly leads to better classification results, but