{"id":374,"date":"2014-03-13T21:08:34","date_gmt":"2014-03-14T01:08:34","guid":{"rendered":"http:\/\/minireference.com\/blog\/?p=374"},"modified":"2014-03-13T21:08:34","modified_gmt":"2014-03-14T01:08:34","slug":"getting-started-with-ml-in-python","status":"publish","type":"post","link":"https:\/\/minireference.com\/blog\/getting-started-with-ml-in-python\/","title":{"rendered":"Getting started with ML in python"},"content":{"rendered":"

Next week I’m interviewing for a Data Scientist position so I figured I better brush up my machine learning skills. I found some neat youtube tutorials [1<\/a>,2<\/a>] on using scikit-learn<\/a>\u00a0so I thought this would be a good place to start.<\/p>\n

From experience, I was expecting that setting up the dev-environment with numpy, scipy, ipython notebook, etc, would take me half a day (compiling and debugging things that don’t work out of the box), but I was pleasantly surprised when a few pip commands later I had a fully functional environment. I’ve pasted the sequence of commands below for all those in case you want to learn yourself some ML too.<\/p>\n

Create a virtualenv<\/h3>\n

The first part is to create an isolated virtualenv for the project. Think of this as “basic python hygiene”: we want to isolate the python libraries used to follow the tutorial from my system-wide python library. (For most people this is just “best practices” but in my case my system-wide site-packages contains outdated versions, and or half-broken dependencies because of the dysfunctional relationship between fink, macports, and homebrew that plays out on my computer.) To setup \u00a0a virtualenv in a given directory and activate it, proceed as follows:<\/p>\n


\n$ cd ~\/Projects\/MLpractice
\n$ virtualenv pyML
\n$ . pyML\/bin\/activate # . is the same as source
\n<\/code><\/p>\n

Install prerequisites<\/h3>\n

Next we’ll install all the prerequisite packages and scikit-learn. Note that the command line starts with (pyML)<\/code> which indicates that pip will install these packages in the pyML virtualenv and not system-wide.<\/p>\n


\n(pyML)$ which python
\n(pyML)$ which pip<\/p>\n

(pyML)$ pip install numpy
\n(pyML)$ pip install pyzmq
\n(pyML)$ pip install ipython[all]
\n(pyML)$ pip install scipy
\n(pyML)$ pip install pyparsing<\/p>\n

$ brew update
\n$ brew install freetype
\n$ brew link --force freetype
\n$ brew install libpng
\n$ brew link --force libpng
\n$ brew install libagg
\n(pyML)$ pip install matplotlib
\n(pyML)$ pip install psutil<\/p>\n

(pyML)$ pip install scikit-learn<\/p>\n

<\/code><\/p>\n

Done<\/h3>\n

Now everything is ready and setup for us.
\nWe can clone the repositories with the example code and start the ipython notebook as follows.<\/p>\n


\n$ git clone git@github.com:jakevdp\/sklearn_scipy2013.git
\n$ git clone git@github.com:ogrisel\/parallel_ml_tutorial.git
\n(pyML)$ cd sklearn_scipy2013\/notebooks\/
\n(pyML)$ ipython notebook --pylab inline
\n<\/code><\/p>\n

Your default browser should open showing you iPython notebooks for the first tutorial.
\nLet the learning begin—both for machine and human alike!<\/p>\n","protected":false},"excerpt":{"rendered":"

Next week I’m interviewing for a Data Scientist position so I figured I better brush up my machine learning skills. I found some neat youtube tutorials [1,2] on using scikit-learn\u00a0so I thought this would be a good place to start. From experience, I was expecting that setting up the dev-environment with numpy, scipy, ipython notebook, […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4,15],"tags":[],"class_list":["post-374","post","type-post","status-publish","format-standard","hentry","category-computers","category-tools"],"_links":{"self":[{"href":"https:\/\/minireference.com\/blog\/wp-json\/wp\/v2\/posts\/374","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/minireference.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/minireference.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/minireference.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/minireference.com\/blog\/wp-json\/wp\/v2\/comments?post=374"}],"version-history":[{"count":0,"href":"https:\/\/minireference.com\/blog\/wp-json\/wp\/v2\/posts\/374\/revisions"}],"wp:attachment":[{"href":"https:\/\/minireference.com\/blog\/wp-json\/wp\/v2\/media?parent=374"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/minireference.com\/blog\/wp-json\/wp\/v2\/categories?post=374"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/minireference.com\/blog\/wp-json\/wp\/v2\/tags?post=374"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}