some more setup instructions

Author: amueller

README.md

@@ -10,10 +10,38 @@ scikit-learn, though you need to adjust the import for everything from the
 ``model_selection`` module, mostly ``cross_val_score``, ``train_test_split``
 and ``GridSearchCV``.
 
+
 This repository provides the notebooks from which the book is created, together
 with the ``mglearn`` library of helper functions to create figures and
 datasets.
 
 For the curious ones, the cover depicts a [hellbender](https://en.wikipedia.org/wiki/Hellbender)
 
+## Setup
+
+To run the code, you need the packages ``numpy``, ``scipy``, ``scikit-learn``, ``matplotlib``, ``pandas`` and ``pillow``.
+Some of the visualizations of decision trees and neural networks structures also require ``graphviz``.
+
+The easiest way to set up an environment is by installing [Anaconda](https://www.continuum.io/downloads).
+
+### Installing packages with conda:
+If you already have a Python environment set up, and you are using the ``conda`` package manager, you can get all packages by running
+
+    conda install numpy scipy scikit-learn matplotlib pandas pillow graphviz
+
+and then *also*
+
+    pip install graphviz
+
+(Explanation: the conda package graphiz is the C library, not the python library)
+
+### Installing packages with pip
+If you already have a Python environment and are using pip to install packages, you need to run
+
+    pip install numpy scipy scikit-learn matplotlib pandas pillow graphviz
+
+You also need to install the graphiz C-library, which is easiest using a package manager.
+If you are using OS X and homebrew, you can ``brew install graphviz``. If you are on Ubuntu or debian, you can ``apt-get install graphviz``.
+Installing graphviz on Windows can be tricky and using conda / anaconda is recommended.
+
 ![cover](cover.jpg)