A couple of old model fitting tricks

I picked up many modeling tricks in graduate school working in a computational neuroscience laboratory. We doing machine learning, but we don't know at the time. We called it "automated model fitting." We used custom (and very finicky) algorithms in MATLAB. Now people are blessed with scikit-learn. These ideas might help out for edge cases:

• Have good "fake" data. "Fake" data allows for testing of the algorithms. There is an art to dummy data, it should be noisy but allow the models to converge.
• Linearly transform the data so all dimensions are within the same order of magnitude. Some models have trouble with weighting noise parameters on different scales.

My recent failures

I fail all the time. I recently failed at completing several MOOCs. I'm one of the unwashed masses that started but didn't finish.

Even through attempting, I learned something from each course. Given my personal value in life-long learning, improvement is my measure of success. Completion is easier to quantify and a more commonly accepted measure of success. A "factory" model of education values completion. A half completed commodity has little value. A factory stops work on a product and ships it. My professional life is continuous delivery. Any incremental improvement adds value to a continuous delivery system. I picked up new viewpoints on existing concepts that improved my understanding of the world through "failing" at MOOCs. I didn't get credit but I got value.