When discussing machine learning development approaches, data scientists often need to ask themselves does this use case apply best for supervised or unsupervised learning? In this episode we break down the strengths and weaknesses of each approach and discuss various use cases to which each one best applies. Melody explores the notion that supervised learning works much like our education system: there's a teacher "supervising" the learning process. Unsupervised learning on the other hand has no correct answers and no teacher. Algorithms are simply fed unlabeled data and left to structure the data in some new, interesting way. Melody, Nikhil, and Saurabh dive into each approach and cite exciting business use cases including autonomous vehicles, Speech2Face, and accelerating ecological research in Serengeti National Park.
For more information, check out our white paper Supervised vs Unsupervised Learning