The question "do I need hard math for ML" often comes up in #machinelearning at irc.freenode.net My point here is: You don't need hard math (most of the times)

The question "do I need hard math for ML" often comes up in #machinelearning at irc.freenode.net My point here is: You don't need hard math (most of the times) because most machine learning methods are already coded in half a dozen different languages. So its similar to fft. You do not need to understand why fft works, just when and how to apply it. The typical machine learning workflow is: Data mining -> feature extraction -> applying a ML method. I often joke that I'm using Weka as a hammer, to check, if I managed to shape the problem into a nail. Now the critical part is feature extraction. Once this is done right, most methods show more or less good results. Just pick the one that fits best in results, time and memory constrains. You might need to recode the method from Java to C to speedup, or to embed it. But this requires nearly no math skills, just code reading, writing and testing skills.