In this episode of Adventures in Machine Learning, the amazing author and course creator Frank Kane entertains our panel with information and examples. Beril Sirmacek, Gant Laborde, Daniel Svoboda, & Charles Wood talk with Frank Kane about recommender systems. The discussion elaborates on collaborative and content based recommendation systems, how they all work and how amazing they can be. Frank’s variety of experience provides fun stories, exciting examples, and a roadmap for beginners filled the complex domain with friendly stories. This episode is a MUST LISTEN for people interested in getting into Machine Learning or recommender systems.

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