Machine Learning (ML) appears to be the ubiquitous go-to solution for a great many modern problems across many domains. But what is really under the hood of a typical ML solution? And, why are so many problems suddenly becoming good ML candidates? This webinar explores non-mathematically the foundational aspects of ML and how they add up to a satisfactory solution. It also highlights the benefits and pitfalls of ML in several scenario exemplars.