How to Predict Music You Love (LIVE)

In this video, we're going to look at several different type of recommender systems in an iPython notebook. Popularity based, item-item collaborative, then user-item collaborative. Then we'll touch on the bleeding edge in deep learning at the end. Also I freestyle. Twice lol. Code for this video: https://github.com/llSourcell/recommender_live More learning resources: http://tech.hulu.com/blog/2016/08/01/cfnade.html https://blogs.msdn.microsoft.com/carlnol/2012/06/23/co-occurrence-approach-to-an-item-based-recommender/ https://www.mapr.com/blog/inside-look-at-components-of-recommendation-engine https://www.ics.uci.edu/~welling/teaching/CS77Bwinter12/presentations/course_Ricci/13-Item-to-Item-Matrix-CF.pdf https://www.analyticsvidhya.com/blog/2016/06/quick-guide-build-recommendation-engine-python/ http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/ Join us in our Slack channel: http://wizards.herokuapp.com/ Please Subscribe. And Like. And comment. That's what keeps me going. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/