 note on recommendation engine libraries you might have noticed that we did not use any readily available packages for building our recommendation system like for every possible taskpython has multiple libraries available for building recommendation engines too but we have refrained from using such libraries because we wanted to give you taste of what goes on behind recommendation engine most of these libraries will take the sparse matrix format as input data and allow you to develop recommendation engines we encourage you to continue with your experimentation with at least one of those librariesas it will give you an idea of exploring different possible implementations of the same problem and the differences those implementations can make some libraries that you can use for such exploration include scikit-surpriselightfmcrabrec_sysetc summary in this we learned about recommendation systemswhich are an important and widely known machine learning application we discovered very popular data source that allowed us to peek inside small section of online music listeners we then started on the learning curve of building different types of recommendation engines we started with simple vanilla version based on popularity of songs after thatwe upped the complexity quotient of our recommendation engines by developing an item similarity based recommendation engine we urge you to extend that recommendation engine using the metadata provided as part of million song database finallywe concluded the by building recommendation engine that takes an entirely different point of view we explored some basics of matrix factorization and learned how very basic factorization method like singular value decomposition can be used for developing recommendations we ended the by mentioning about the different libraries that you can use to develop sophisticated engines from the dataset that we created we would like to close this by further underlining the importance of recommendation enginesespecially in the context of online content delivery an uncorroborated fact that' often associated with recommendation systems is that " of netflix movie viewings are through recommendationsaccording to wikipedianetflix has an annual revenue of around $ billion even if half of that is coming from movies and even if the figure is instead of %it means that around $ billion of netflix revenue can be attributed to recommendation engines although we will never be able to verify these figuresthey are definitely strong argument for recommendation engines and the value they can generate