code descr

Author: rasbt

README.md

@@ -34,7 +34,8 @@ If you are interested in keeping in touch, I have quite a lively twitter stream
 
 You can view the code examples as IPython Notebooks by clicking `ipynb`. Until GitHub-rendered IPyNb support internal document links, I recommend to use the alternative `nbviewer` links. **Please note that these are just the code examples accompanying the book that I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.**
 
-Excerpts from the [Foreword](./docs/foreword_ro.pdf) and [Preface](./docs/preface_sr.pdf)  
+Excerpts from the [Foreword](./docs/foreword_ro.pdf) and [Preface](./docs/preface_sr.pdf)
+  
 1. Machine Learning - Giving Computers the Ability to Learn from Data [[dir](./code/ch01)] [[ipynb](./code/ch01/ch01.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb)]
 2. Training Machine Learning Algorithms for Classification [[dir](./code/ch02)] [[ipynb](./code/ch02/ch02.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch02/ch02.ipynb)]
 3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[dir](./code/ch03)] [[ipynb](./code/ch03/ch03.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch03/ch03.ipynb)]