Note: I usually write more technical posts, this is an opinion piece. And you know what they say: opinions are like feet, everybody’s got a couple.
Machine learning is simply statistics
A lot of buzz words nowadays. Data Science, business intelligence, machine learning, deep learning, statistical learning, predictive analytics, knowledge discovery, data mining, pattern recognition. Surely you can think of a few more. So many you can fill a chapter explaining and discussing the difference (e.g. Data science for dummies).
But really, we are all after the same thing. The thing being: extracting knowledge from data. Perhaps it is because we want to explain something, perhaps it is because we want to predict something; the reason is secondary. All those terms fall under one umbrella which is modern statistics, period. I freely admit that the jargon is different. What is now dubbed feature space in machine learning literature is simply independent, or explanatory variables in the statistical literature. What one calls softmax classifier in the deep learning context, another calls multinomial regression in a basic 1-0-1 statistics course. Feature engineering? Call it variable transformation rather.