of courseusing mds requires an understanding of the individual feature' unitsmaybe we are using features that cannot be compared using the euclidean metric for instancea categorical variableeven when encoded as an integer ( red circle blue star green triangleand so on)cannot be compared using euclidean (is red closer to blue than to green?but once we are aware of this issuemds is useful tool that reveals similarities in our data that otherwise would be difficult to see in the original feature space looking bit deeper into mdswe realize that it is not single algorithmbut family of different algorithmsof which we have used just one the same was true for pcaand in case you realize that neither pca nor mds solves your problemjust look at the other manifold learning algorithms that are available in the scikit-learn toolkit summary we learned that sometimes we can get rid of all the features using feature selection methods we also saw that in some cases this is not enoughand we have to employ feature extraction methods that reveal the real and the lower-dimensional structure in our datahoping that the model has an easier game with it we have only scratched the surface of the huge body of available dimensionality reduction methods stillwe hope that we have got you interested in this whole fieldas there are lots of other methods waiting for you to pick up at the endfeature selection and extraction is an artjust like choosing the right learning method or training model the next covers the use of juga little python framework to manage computations in way that takes advantage of multiple cores or multiple machines we will also learn about aws the amazon cloud