You know, I really should add a post soon about algorithms, papers, and textbooks. You make an important point which the first responder highlighted, "avoiding the destruction of business value by misapplying ML/statistics."
I understand the math behind what I do, but it's not a fair assumption to think that everyone reading my post will be motivated to pick up and understand the math before they start applying the tools.
Especially with tools like scikit-learn and orange, it's especially easy to misapply ML and statistics or simply approach a problem without understanding the tools and come out with something that looks plausible to the untrained eye.
Key to the reason that you should understand your tools, including the math that underlies them, is that you should be able to look at the results of your work and know if there's something "off". And beyond that the underlying understanding of the math involved gives you the tools you need to debug.
I understand the math behind what I do, but it's not a fair assumption to think that everyone reading my post will be motivated to pick up and understand the math before they start applying the tools.
Especially with tools like scikit-learn and orange, it's especially easy to misapply ML and statistics or simply approach a problem without understanding the tools and come out with something that looks plausible to the untrained eye.
Key to the reason that you should understand your tools, including the math that underlies them, is that you should be able to look at the results of your work and know if there's something "off". And beyond that the underlying understanding of the math involved gives you the tools you need to debug.