[Edu-sig] probability and statistics demo for kids

[Wes Turner]

On Saturday, February 24, 2018, kirby urner <kirby.urner at 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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