EPISODE · Oct 22, 2016 · 6 MIN
Recommendation Systems - Learn Python for Data Science #3
from Siraj Raval
In this video, we build our own recommendation system that suggests movies a user would like in 40 lines of Python using the LightFM recommendation library. I start off by talking about why we need recommendation systems, then we dive straight into installing our dependencies and writing our script. The coding challenge for this video is here: https://github.com/llSourcell/recommender_system_challenge The winner of last weeks coding challenge (Rohan Verma): https://twitter-sentiment-csv.herokuapp.com/ https://t.co/4eg8UdlaSB The runner up (Arnaud Delauney): https://github.com/arnauddelaunay/twitter_sentiment_challenge I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The LightFM Python Library: https://github.com/lyst/lightfm/tree/master/lightfm Some great learning resources on recommender systems: http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/ https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/ http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/ http://blog.manugarri.com/a-short-introduction-to-recommendation-systems/ Best book to become a Python God: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. 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/
What this episode covers
In this video, we build our own recommendation system that suggests movies a user would like in 40 lines of Python using the LightFM recommendation library. I start off by talking about why we need recommendation systems, then we dive straight into installing our dependencies and writing our script. The coding challenge for this video is here: https://github.com/llSourcell/recommender_system_challenge The winner of last weeks coding challenge (Rohan Verma): https://twitter-sentiment-csv.herokuapp.com/ https://t.co/4eg8UdlaSB The runner up (Arnaud Delauney): https://github.com/arnauddelaunay/twitter_sentiment_challenge I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ The LightFM Python Library: https://github.com/lyst/lightfm/tree/master/lightfm Some great learning resources on recommender systems: http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/ https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/ http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/ http://blog.manugarri.com/a-short-introduction-to-recommendation-systems/ Best book to become a Python God: https://learnpythonthehardway.org/ Please share this video, like, comment and subscribe! That's what keeps me going. 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/
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Recommendation Systems - Learn Python for Data Science #3
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