Update README.rst

ibayer · Jan 14, 2016 · 6380105 · 6380105
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     Immanuel Bayer (2015): fastFM: A Library for Factorization Machines http://arxiv.org/abs/1505.00641
 
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 fastFM: A Library for Factorization Machines
 ============================================
 
+.. image:: https://travis-ci.org/ibayer/fastFM.svg?branch=master
+   :target: https://travis-ci.org/ibayer/fastFM
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+.. image:: https://img.shields.io/badge/platform-OSX|Linux-lightgrey.svg
+  :target: https://travis-ci.org/ibayer/fastFM
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+.. image:: https://img.shields.io/pypi/l/Django.svg   
+   :target: https://travis-ci.org/ibayer/fastFM
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 This repository allows you to use Factorization Machines in **Python** (2.7 & 3.5) with the well known **scikit-learn API**.
 All performence critical code as been written in C and wrapped with Cython. fastFM provides
 stochastic gradient descent (SGD) and coordinate descent (CD) optimization routines as well as Markov Chain Monte Carlo (MCMC) for Bayesian inference.