Machine Learning for Dummies

https://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=IMM14209USEN

by tosh • 9 years ago

147 25 9 years ago

www-01.ibm.com

dahart• 9 years ago

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.

mathattack• 9 years ago

Oh the irony of this coming from IBM!

coherentpony• 9 years ago

Why is it ironic?

david-cako3• 9 years ago

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

zodPod• 9 years ago

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.

platz• 9 years ago

I think people are drawn to ML because they've heard they do not need to pick features because the algorithms will find the features for them.

pakl• 9 years ago

Cleaning data eliminates the driving force to develop truly autonomous algorithms.

kmax12• 9 years ago

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/) 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.

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

tgarma1234• 9 years ago

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.

wonderous• 9 years ago

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%...

vinn1242• 9 years ago

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.

sergers• 9 years ago

I knew of a few people who left our company to work in the Watson healthcare division sales side, post purchase of Merge Healthcare.

Everyone in the industry, assisted diagnosis, thought this was gonna be a game changer, with IBM with merge dominating.

They lasted about 9 months before coming back or going to another company. Even what they were briefed to sell on, IBM couldn't exactly do all it was marketing.

chillee• 9 years ago

"second to none"?

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

vinn1242• 9 years ago

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

neves• 9 years ago

Any mobi version of the book?

r0f1• 9 years ago

And not a single algorithm was explained..

killjoywashere• 9 years ago

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

jacek• 9 years ago

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

Machine Learning for Dummies

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