On Saturday, February 24, 2018, kirby urner <kirby.ur...@gmail.com> wrote:
> > In terms of Machine Learning more generally, I want to give special > recognition to Jake VanderPlas, an astronomer who dives deep into > scikit-learn in some multi-hour Youtube-shared tutorials. > > Example: > https://youtu.be/L7R4HUQ-eQ0 > > His excellent keynote at Pycon2017: > https://youtu.be/ZyjCqQEUa8o > > Jake does a super-excellent job of showing off the internal consistency of > the scikit-learn API, where you can basically use the same code while just > swapping in one classifier or regressor for another. > > He also speaks the jargon pretty flawlessly, to my ears at least, in terms > of what's a feature (label) and what's an observation etc., going into both > supervised and unsupervised learning scenarios (scikit-learn handles both). > > Bravo Jake. > +1. "Python Data Science Handbook" (by Jake VanderPlas) is available in print and as free Jupyter notebooks: https://github.com/jakevdp/PythonDataScienceHandbook It covers IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn. > Allen Downey has great complementary tutorials which go deeper into the > statistical thinking behind these ML models. ThinkBayes is fantastic. > > It's tempting to just mindlessly throw models at data looking for a best > fit, and maybe that's all some underpaid cube farmer has time for, but > VanderPlas, along with Downey, wisely counsels against that. > > Stats more than most is a minefield of pitfalls, such as overfitting. If > your aim is authentic research, then mindless model-slinging will quickly > come up against its own limitations. That's the message I keep getting > from experts in the field. > > Kirby > > PS: thanks to Steve Holden, I got to visit the astronomy world up close, > the form of the Hubble Space Telescope instrumentation team, eager for > Python knowledge. These were already programmers, experts with IDL, but > IDL is not the hard currency Python is, in the wider job market. For many > reasons, astronomers can't put all their eggs in one basket. The Python > ecosystem has been a godsend. > > > > > > >
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