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Code & Data for Introduction to Machine Learning with Scikit-Learn

Project README

Introduction to Machine Learning with Scikit-Learn

Code & Data for Introduction to Machine Learning with Scikit-Learn

Scikit-Learn Cheat Sheet

Installing Scikit-Learn with pip

See the full installation instructions for more details; these are provided for convenience only.

Scikit-Learn requires:

  • Python >= 2.6 or >= 3.3
  • Numpy >= 1.6.1
  • SciPy >= 0.9

Once you have installed pip (the python package manager):

Mac OS X

This should be super easy:

pip install -U numpy scipy scikit-learn

Now just wait! Also, you have no excuse not to do this in a virtualenv.

Windows

Install numpy and scipy with their official installers. You can then use PyPi to install scikit-learn:

pip install -U scikit-learn

If you're having trouble, consider one of the unofficial windows installers or anacondas (see the Scikit-Learn page for more).

Ubuntu Linux

Unfortunately there are no official binary packages for Linux. First install the build dependencies:

sudo apt-get install build-essential python-dev python-setuptools \
    python-numpy python-scipy \
    libatlas-dev libatlas3gf-base

Then you can build (hopefully) Scikit-learn with pip:

pip install --user --install-option="--prefix=" -U scikit-learn

Keep in mind however, that there are other dependencies and might be issues with ATLAS and BLAS - see the official installation for more.

Open Source Agenda is not affiliated with "DistrictDataLabs Machine Learning" Project. README Source: DistrictDataLabs/machine-learning
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