I'm mixed on this article. While I strongly agree that data analysis trumps math for applying machine learning, I think there's a middle ground the article is m

I'm mixed on this article. While I strongly agree that data analysis trumps math for applying machine learning, I think there's a middle ground the article is missing. Like programming, machine learning has its 10xers, and I've worked with several. There's one thing they all have in common (beyond experience): they can think like the model. They've read a bunch of ML papers, get the intuition behind them, and envision from start to finish how data can be captured by various models. Beyond a small amount of hyperparameter optimization they often build a great model on the first or second take, and can put out production models in days (which would normally take novices weeks or even months). This requires math knowledge, because ML models are built on top of math. Even in higher level ML packages that do a lot of the work for you, novices get stuck in dead ends where their models don't work and they don't know why. The 10xers drive right through it because they know why their model isn't working. When I interview for my team, I usually have non-junior candidates describe a ML algorithm they would use for a specific problem, then ask why certain things could go wrong - e.g. if they say they would use a logistic regression model to predict an action, I ask why the model may be returning the same score for every test case. A good candidate would understand that this means the coefficients are getting pushed to zero, and would list off reasons why this could be occurring (too much regularization, underpowered data, constant target variable, etc). You learn these things from experience, but you understand them because you know the math behind the models.