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Could someone with industry/academic experience in ML comment on the quality and reliability of the resources in the repo?

tdaltonc 11y ago
The quality of Scikit-Learn? It's not bleeding-edge but it's very well tested and documented. Quite good quality. No one gets fired for using Scikit.
- hangtwenty 11y ago
  I'm curious if you can speak more to this, or share any resources about it. It seems clear that scikit-learn is a good fit for this kind of hacking-learning. If there's a way I can throw in a sentence (with link to more detail), giving context about where it sits in the eyes of experts ... Would be nice.
  - argonaut 11y ago
    What is there to be worried about? scikit-learn is a solid, tested implementation of most machine learning algorithms. If you're doing work in Python and want to run your data through a standard ML algorithm, and the algo is implemented by scikit-learn, then just use scikit-learn. If it isn't implemented by scikit-learn, you find some other implementation or implement it yourself. Experts use all sorts of things: MATLAB, R, Python (with scikit-learn), etc.
    - hangtwenty 11y ago
      What you're saying -- actually every sentence of your comment -- was my existing impression. tdaltonc said "No one gets fired for using Scikit." Maybe I read too much into this comment, but it seemed to have a negative tone. So I got the impression that tdaltonc might have more to say about it. Maybe not though!
stared 11y ago
It's a nice list of resources for starting. General tools he mentions are both easy to start and are used in practice; also, I like the overview part. But most importantly - it's not a dump of all possible links, making a daunting list "I will never go through". Source: I run workshops introducing to ML and Big Data (http://workshops.deepsense.io/ http://workshops.deepsense.io/, next one in London) and I made a lot of choices converging with this one (Python + scikit-learn, everything in Jupyter Notebook, etc). Also, a lot of links there is already in my delicious list of things I am sending to friends wanting to jump into data science (and many of them were already on the HN main page). BTW: See also discussion on the same post on DataTau: http://www.datatau.com/item?id=10093 http://www.datatau.com/item?id=10093

tdaltonc 11y ago

The quality of Scikit-Learn? It's not bleeding-edge but it's very well tested and documented. Quite good quality. No one gets fired for using Scikit.

hangtwenty 11y ago
I'm curious if you can speak more to this, or share any resources about it. It seems clear that scikit-learn is a good fit for this kind of hacking-learning. If there's a way I can throw in a sentence (with link to more detail), giving context about where it sits in the eyes of experts ... Would be nice.
- argonaut 11y ago
  What is there to be worried about? scikit-learn is a solid, tested implementation of most machine learning algorithms. If you're doing work in Python and want to run your data through a standard ML algorithm, and the algo is implemented by scikit-learn, then just use scikit-learn. If it isn't implemented by scikit-learn, you find some other implementation or implement it yourself. Experts use all sorts of things: MATLAB, R, Python (with scikit-learn), etc.
  - hangtwenty 11y ago
    What you're saying -- actually every sentence of your comment -- was my existing impression. tdaltonc said "No one gets fired for using Scikit." Maybe I read too much into this comment, but it seemed to have a negative tone. So I got the impression that tdaltonc might have more to say about it. Maybe not though!

stared 11y ago

It's a nice list of resources for starting. General tools he mentions are both easy to start and are used in practice; also, I like the overview part. But most importantly - it's not a dump of all possible links, making a daunting list "I will never go through". Source: I run workshops introducing to ML and Big Data (http://workshops.deepsense.io/ http://workshops.deepsense.io/, next one in London) and I made a lot of choices converging with this one (Python + scikit-learn, everything in Jupyter Notebook, etc). Also, a lot of links there is already in my delicious list of things I am sending to friends wanting to jump into data science (and many of them were already on the HN main page). BTW: See also discussion on the same post on DataTau: http://www.datatau.com/item?id=10093 http://www.datatau.com/item?id=10093