Recommendation systems are one topic that most Data Scientists are familiar with. However, there is a lack of entry level, general view tutorials on the python ecosystem. This workshop will start with the basics, and implement recommendation engines with different degrees of complexity, and talk about Similarity Index , Content filtering and Collaborative filtering.
Recommendation systems (or engines), as their name says, are systems designed to recommend. The majority of us interact with them on a daily basis,
In this talk I will go through and explain different approaches to recommendation engines, with code examples highlighting the critical code to build each one of the methodologies I will talk about.
More specifically I will talk about the concept of similarity, and elaborate on content filtering and collaborative filtering methods.