Recommendation systems are in use everywhere, and yet I found it difficult to find helpful material on implementing them. This workshop aims to make it both easy to understand and apply item-item collaborative filtering. It will be 30 minutes of theory and one hour of practice. This will be hands on using Jupyter notebooks—so please install Anaconda ahead of time.
About the presenter:
Merlyn is an experienced software engineer working at New Relic. He studied machine learning in Berlin where he also worked as a research assistant on SVMs. He's particularly keen on reinforcement learning and on implementing AIs that compete in simulated environments. Merlyn regularly does "ML katas" (https://github.com/curious-attempt-bunny/ml-katas) with folks inside and outside of work—which you're welcome to join. He also always has a side project going that he'll gladly discuss with you.