Assembling a Python Machine Learning Toolkit by Sujit Pal.
From the post:
I had been meaning to read Peter Harrington’s book Machine Learning In Action (MLIA) for a while now, and I finally finished reading it earlier this week (my review on Amazon is here). The book provides Python implementations of 8 of the 10 Top Algorithms in Data Mining listed in this paper (PDF). The math package used in the examples is Numpy, and the charts are built using Matplotlib.
In the past, the little ML work I have done has been in Java, because that was the language and ecosystem I knew best. However, given the experimental, iterative nature of ML work, its probably not the most ideal language to use. However, there are lots of options when it comes to languages for ML – over the last year, I have learned Octave (open-source version of MATLAB) for the Coursera Machine Learning class and R for the Coursera Statistics One and Computing for Data Analysis classes (still doing the second one). But because I know Python already, Python/Numpy looks easier to use than Octave, and Python/Matplotlib looks as simple as using R graphics. There is also the pandas package which provides R-like features, although I haven’t used it yet.
Looking around on the net, I find that many other people have reached similar conclusions – ie, that Python seems to be the way to go for initial prototyping work in ML. I wanted to set up a small toolbox of Python libraries that will allow me to do this also. I settled on an initial list of packages based on the Scipy Superpack, but since I am still on Mac OS (Snow Leopard) I could not use the script from there. There were some issues I had to work through to make this to work, so I document this here, so if you are in the same situation this may help you.
Unlike the Scipy Superpack, which seems to prefer versions that are often the bleeding edge development versions, I decided to stick to the latest stable release versions for each of the libraries. Here they are:
Sujit’s post will save you a few steps in assembling your Python machine learning toolkit.
Pass it on.
Here’s the URL for Sujit’s post:
http://sujitpal.blogspot.com/2013/01/assembling-python-machine-learning.html
Comment by CapnKirk — January 24, 2013 @ 8:04 am
Thanks! Corrected!
Comment by Patrick Durusau — January 24, 2013 @ 8:15 pm