
Machine Learning for Humans: A Beginner's Guide to AI/ML - vmaini
https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12
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minimaxir
A slightly-off topic note:

There's often complaints on posts like these that adding Machine Learning to
any HN submission title will result in instant upvotes.

Yesterday, I built a deep learning model which can predict the optimal time to
submit a post to Hacker News to maximize point score, and also predict the
general probability of hitting the front page. In the context of this post,
incidentally, the title is too verbose; both the point potential and and prob
of hitting the front page is higher if only using the second half (w/o the
explicit "Machine Learning" invocation):
[http://i.imgur.com/5vOTTXo.png](http://i.imgur.com/5vOTTXo.png)

~~~
pen2l
Is that Jupyter?

I'm kind of a python/ML newb, am curious to know: when doing ML stuff, do you
guys usually do it interactively with Jupyter? The whole process of
preprocessing, graphing, reducing dimensions, ML... do you prefer to do it
one-by-one in Jupyter or Jupyter-like envrionment vs. just make one big python
script? Just curious about common workflows.

~~~
minimaxir
Correct. For common workflows, see my post comparing Jupyter Notebooks and R
Notebooks:
[http://minimaxir.com/2017/06/r-notebooks/](http://minimaxir.com/2017/06/r-notebooks/)

 _Never_ do data analysis in "one big Python script" if you value your sanity.

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dchuk
This looks pretty nice on a brief skim. Would love if it was packaged as an
ebook rather than a bunch of medium posts.

~~~
esfandia
They have a link at the bottom of the page where you can send them an email to
get a pdf. You might receive more than just the pdf...

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SoMisanthrope
IMHO HN is getting saturated with these articles. It's all good, I know that's
it's a popular subject. So, just and observation.

~~~
blennon
I agree with this. If we're talking about the same thing, many of these are
"Machine learning for hackers" type articles and only give a superficial
exposure of machine learning with the implicit promise of quick and easy
mastery. The problem is, there's really only one way to master this stuff and
it's to open up a textbook and study hard. You really need to get a rigorous
treatment of the theory to understand machine learning.

~~~
auvrw
there are chapter endnotes and an appendix that looks very helpful.

meta-texts like this one are a must, because the subject is so broad.
moreover, i find a lot of the oft-recommended books in this area very noisy on
the theory side.

i imagine all of all of the available resources will improve in quality and
become more standard over time.

