Training Materials

##Recommender System Training Materials There are many wonderful online resources to learn more about recommender systems. A few introductory sources for a general overview of recommender systems include:

• Introduction to Recommender Systems: A six week Coursera course from the University of Minnesota with hands-on projects, and around 100 hours of topical lectures, interviews and guest lectures with experts from both academia and industry.
• Recommender Systems Handbook: (Ricci, F., Rokarch, L., Shapria, B., Kantor, P.B., Springer 2011): A comprehensive handbook on recommender systems including techniques, applications and evaluation of recommender systems, interacting with recommender systems, recommender systems and communities, and advanced algorithms. There is also a newer addition by the same editors which was very recently published.
• Recommender Systems: A 4 hour tutorial slide deck by Xavier Amatriain on recommender systems including lessons learned from the Netflix Prize challenge.

##Python and Spark Training Materials There are a number of ways to get started with Spark. You could download and run according to the Spark 1.5.2 Quick Start guide, or this blog describes. You can also download a docker container which contains Spark and iPython notebooks as illustrated on our GitHub or blog. This is a really easy way to try out the application.

There was also a hands-on training exercise at Spark Summit 2014 which will step you through Spark SQL, Spark Streaming, MLlib and GraphX. Some of the information covered is shown in the CF Test notebook, but the 'Movie Recommendation with MLlib' exercise is particularly useful.

There are a lot of good resources too if you are just getting started with Python. There is a 'Learn to Program and Analyze Data with Python' specialization on Coursera, a codecademy course, and tutorials on learnpython, tutorialspoint, and python.org just to name a few.