summary in this we stepped out of our comfort zone when we built music genre classifier not having deep understanding of music theoryat first we failed to train classifier that predicts the music genre of songs with reasonable accuracy using fft but then we created classifier that showed really usable performance using mfc features in both caseswe used features that we understood only so much as to know how and where to put them into our classifier setup the one failedthe other succeeded the difference between them is that in the second casewe relied on features that were created by experts in the field and that is totally ok if we are mainly interested in the resultwe sometimes simply have to take shortcuts--we only have to make sure to take these shortcuts from experts in the specific domains and because we had learned how to correctly measure the performance in this new multiclass classification problemwe took these shortcuts with confidence in the next we will look at how to apply techniques you have learned in the rest of the book to this specific type of data we will learn how to use the mahotas computer vision package to preprocess images using traditional image processing functions