In this talk, we will start with the broad landscape of Machine Learning, to understand the most common applications of the technology. Our focus will be on applications of classical machine learning, but we will also take a look at the astounding reach of Deep Learning that seems like science-fiction in the making. Our next goal will be to break down Machine Learning into its core components — Statistics, Linear Algebra, Numerical Methods, Optimization, and Algorithms — to understand how to connect to this technology from various academic backgrounds. Finally, we will like to build an intuitive understanding of a slightly broader domain — Data Science — to see how it fits into the context of any scientific and technological discipline. This talk is definitely not for practitioners or researchers of Machine Learning; it is more for audience with little or no background.