At the very peak of the most recent 2016, Gartner Hype Cycle resides Machine Learning, which subsumes what used to be categorised as Big Data, Data Science, Artificial Intelligence, Data Mining, or Predictive Analytics. I would even include Statistics and Nonlinear Dynamics, two more traditional fields which continue to remain important, within the broad definition. While there may be slight technical differences, in common parlance they mean the same thing: the ability to use computers (i.e., "machines") and sophisticated mathematics to extract actionable predictive insights from data (i.e., "learn"). In our day and age, almost all mathematicians, statisticians, and physicists use computers, while all computer scientists who work with data usually learn the basics of traditional data science disciplines such as statistics and applied mathematics, nonlinear dynamics and network science in physics, signal processing in engineering, and econometrics in economics. Thus, any difference between the Machine Learning and what may have been once called traditional data sciences has been fast disappearing.