[dustintran]

I strongly disagree with not using linear models, at least to build some theory and intuition before continuing with more sophisticated algorithms. What I find to be more egregiously misused when doing machine learning in practice is that everyone too often flocks to the state of the art with little understanding why. There's no reason for example to spend weeks (or months) tuning a incredibly deep neural network if the current predictive ability is enough and there are higher priority matters to work on.

Moreover, there's just too much of an emphasis on prediction. Design and analysis of experiments, handling missing data and the context of the data sets, and quantifying one's uncertainty about parameters in a principled manner for robust estimators are very underappreciated skills in the community. Using p values arbitrarily and "95% confidence intervals" based on an unchecked normal approximation is incredibly more harmful than not doing anything at all. There's just so much more to machine learning than supervised learning.

[kylebgorman]

In natural language processing, we can get close to state of the art performance on nearly every major task with a linear model; usually, the feature sets contain what are essentially conjunctions of features, but these are chosen by hand, by domain experts, rather than produced with, say, a polynomial kernel.

[chengtao]

To add on top of that, even with great data analysis skill, I had another blog-post talking about it requires all the product, data, and engineering skills together to make a good data science team. http://ml.posthaven.com/why-building-a-data-science-team-is-...