Data Science and Machine Learning have rocked the world in a big way, especially in last 5–6 years. As put by Hal Varian, the chief economist at Google, “the sexy job in the next 10 years will be statisticians. People think I’m joking, but who would’ve guessed that computer engineers would’ve been the sexy job of the 1990s?”

The words —_ “Data Scientist : Sexiest Job of 21st Century”_ have been quoted an uncountable number of times to the extent that it now sounds very boring! As per the study done by LinkedIN in 2017, Machine Learning Engineer and Data Scientist were on the top in Emerging jobs market and the trend is even stronger today.

Everyone wants to join the bandwagon and the plethora of online courses on Data Science are facilitating this desire, all in a good way. (I have learned a lot from Coursera, Udacity and Analytics Vidhya). Almost every software engineer out there is trying to acquire at least some skills around Data Science, Machine Learning and Artificial Intelligence. In the heat of developing these new skills there is often a tendency to apply Machine Learning or Data Science approach to problems or use cases where it is not needed. This article is an attempt to describe the framework of judging when and why to use Machine Learning and when and why not to use it.

#artificial-intelligence #programming #data-science #machine-learning