initial commit

.gitignore

@@ -52,3 +52,9 @@ docs/_build/
 
 # PyBuilder
 target/
+
+# Virtual Environment
+venv
+
+# Mac Stuff
+.DS_Store

README.md

@@ -1,2 +1,47 @@
-# machine-learning
-Code & Data for Introduction to Machine Learning with Scikit-Learn
+# Introduction to Machine Learning with Scikit-Learn
+
+**Code & Data for Introduction to Machine Learning with Scikit-Learn**
+
+[![Scikit-Learn Cheat Sheet](docs/img/cheat_sheet.png)](http://scikit-learn.org/stable/tutorial/machine_learning_map/)
+
+## Installing Scikit-Learn with pip
+
+See the full [installation instructions](http://scikit-learn.org/stable/install.html) 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](http://numpy.scipy.org/) and [scipy](http://www.scipy.org/) 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.

docs/index.md

@@ -0,0 +1,47 @@
+# Introduction to Machine Learning with Scikit-Learn
+
+**Code & Data for Introduction to Machine Learning with Scikit-Learn**
+
+[![Scikit-Learn Cheat Sheet](img/cheat_sheet.png)](http://scikit-learn.org/stable/tutorial/machine_learning_map/)
+
+## Installing Scikit-Learn with pip
+
+See the full [installation instructions](http://scikit-learn.org/stable/install.html) 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](http://numpy.scipy.org/) and [scipy](http://www.scipy.org/) 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.

mkdocs.yml

@@ -0,0 +1 @@
+site_name: Introduction to Machine Learning with Scikit-Learn

requirements.txt

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+numpy==1.9.1
+scikit-learn==0.15.2
+scipy==0.15.1
+wsgiref==0.1.2