About this course
This project-based course shows programmers of all skill levels how to use machine learning to build programs that can make recommendations—like recommending new products.
Welcome
1m 1sWhat you should know before watching this course
22sUsing the exercise files
27sSet up environment
2m 15sWhat is a recommendation system?
1m 22sWhat can you do with recommendation systems?
1m 7sCool uses of recommendation systems
1m 32sContent-based recommendations: Recommending based on product attributes
2m 45sCollaborative filtering: Recommending based on similar users
2mIntroduction to NumPy, SciPy, and pandas
1m 21sThink in vectors: How to work with large data sets efficiently
2m 48sExplore our product recommendation data set
2m 14sRepresent product reviews as a matrix
2m 24sRecommend by predicting missing user ratings
1m 36sA simple way to predict missing user ratings
4m 28sLatent representations of users and products
2m 41sCode the recommendation system
3m 6sHow matrix factorization works
2m 29sUse latent representations to find similar products
4m 19sExplore our system’s recommendations
3m 22sUse regularization
1m 52sMeasure recommendation accuracy
3m 19sMake recommendations for existing users
2m 24sHow to handle first-time users
4m 7sFind similar products
1m 59sWrap up
47s