
Machine Learning for Dummies - tosh
https://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=IMM14209USEN
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dahart
For anyone curious whether they should download and read the book, this is a
high level summary, the cliff notes of applying ML/AI, aimed at managers. It
won't do much for a programmer looking to learn neural networks, but it will
give you an overview of what they're being used for and some things to watch
out for if you have ML programmers on your team.

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vinn124
i cringe every time i see "machine learning" and "ibm" in the same sentence.
it's not because ibm research is subpar (if anything, id consider them second
to none) but because their marketing efforts represents everything wrong with
the current ai cycle - that is, selling magic beans to nontechnical and
helpless buyers.

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chillee
"second to none"?

In ML at least, I'd consider them second to many many companies.

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vinn124
theres research outside ai/ml - it's a big world out there.

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jacek
If someone wants a gentle and high quality introduction to Machine Learning, I
can't recommend " Hands-On Machine Learning with Scikit-Learn and TensorFlow"
by Aurélien Géron [1] enough. I wish I had this book when I was starting. It
explains everything from data engineering, through how algorithms work, to
practical applications. Everything in Python 3, covering pandas, scikit-learn,
tensorflow. It is absolutely wonderful!

[1]
[http://shop.oreilly.com/product/0636920052289.do](http://shop.oreilly.com/product/0636920052289.do)

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zodPod
I think one of the hardest parts of ML is not learning how to write it but
learning how to pick features and clean data. So, assuming this is a run down
of types of ML algorithms (which is how most "learn ML" things seem to go)
this is not going to be much more helpful for someone who is still trying to
figure it all out. It's important to know these things but knowing the actual
algorithms is kind of the low hanging fruit. Data organization and features
are the real complex pieces of most ML.

~~~
kmax12
This is absolutely correct! After spending a lot of time trying to understand
why building machine learning models is so difficult, I came to the same
conclusion that "feature engineering" is the key to building high performing
models.

While feature engineering's importance is generally recognized [0], it's
unfortunate that there aren't more tools and formal methods for applying it.
Personally, I am a developer of an open source python library called
Featuretools
([https://github.com/featuretools/featuretools/](https://github.com/featuretools/featuretools/))
that is trying to change this for tabular and multi-table datasets. We are
working hard to make automated feature engineering available to everyone and
have a list of demos for people to try here:
[https://www.featuretools.com/demos](https://www.featuretools.com/demos).

It's also worth noting that deep learning is changing the need for feature
engineering. However, it primarily works in cases where you don't need
interpretable features and you have plenty of data. This means that it's
biggest success have been in images, audio, and text problems. For all other
use cases feature engineering is still a necessary step for applying machine
learning.

[0] [https://bit.ly/things_to_know_ml](https://bit.ly/things_to_know_ml)

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tgarma1234
This comment/repo should be it's own HN post as it is vastly more interesting
than the post you are commenting on. Thanks for the links.

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wonderous
IBM just self-publishes a bunch of these IBM brand guides under the “for
dummies” brand and pays for using the “For Dummies” logo:

[https://www.google.com/search?q=site:ibm.com+%22for+dummies%...](https://www.google.com/search?q=site:ibm.com+%22for+dummies%22+ext:pdf)

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mathattack
Oh the irony of this coming from IBM!

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coherentpony
Why is it ironic?

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david-cako
Probably because they've spent the last 7 years building buzz around machine
learning vaporware.

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murukesh_s
What do you find as vaporware? I don't think they are offering vaporware, but
their marketing is doing lot of hype hoping to enter into the startup
ecosystem, but are failing miserably due to their traditional focus on selling
to large corporates. I have attended IBM sponsored conferences and I was
floored by the the reach they have for their cloud offering, especially on
large firms and Govts. Agree that Bluemix, as an offering is nowhere as
polished as AWS is, but they do have a good business going around it.

~~~
david-cako
I was speaking of Watson. It seems like every time it comes up someone who was
tasked with investigating it for their company or someone who actually worked
at IBM comes along and talks about how big of a joke it actually is.

I've heard good things about Bluemix.

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wooshy
I was one of those people tasked with investigating Watson and I had the exact
experience you described. It was a huge joke. Tried out Bluemix as well, it
wasn't a huge joke but it wasn't great either.

~~~
murukesh_s
What was lacking in Watson? Lack of features or lack of accuracy? I had
integrated Watson in on of the libraries in a platform I was building and
found it reasonable. Haven't compared it extensively with other offerings from
Microsoft or AWS, so interested in knowing if you have..

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r0f1
And not a single algorithm was explained..

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killjoywashere
In other news, IBM welcomes the generation of management that scooted through
college on the Dummy series. OMFH.

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neves
Any mobi version of the book?

