README.md in svmkit-0.1.1 vs README.md in svmkit-0.1.2

@@ -1,7 +1,11 @@
 # SVMKit
 
+[![Build Status](https://travis-ci.org/yoshoku/SVMKit.svg?branch=master)](https://travis-ci.org/yoshoku/SVMKit)
+[![Gem Version](https://badge.fury.io/rb/svmkit.svg)](https://badge.fury.io/rb/svmkit)
+[![BSD 2-Clause License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://github.com/yoshoku/SVMKit/blob/master/LICENSE.txt)
+
 SVMKit is a library for machine learninig in Ruby.
 SVMKit implements machine learning algorithms with an interface similar to Scikit-Learn in Python.
 However, since SVMKit is an experimental library, there are few machine learning algorithms implemented.
 
 ## Installation
@@ -21,10 +25,11 @@
     $ gem install svmkit
 
 ## Usage
 
 Training phase:
+
 ```ruby
 require 'svmkit'
 require 'libsvmloader'
 
 samples, labels = LibSVMLoader.load_libsvm_file('pendigits', stype: :dense)
@@ -44,9 +49,10 @@
 File.open('trained_transformer.dat', 'wb') { |f| f.write(Marshal.dump(transformer)) }
 File.open('trained_classifier.dat', 'wb') { |f| f.write(Marshal.dump(classifier)) }
 ```
 
 Testing phase:
+
 ```ruby
 require 'svmkit'
 require 'libsvmloader'
 
 samples, labels = LibSVMLoader.load_libsvm_file('pendigits.t', stype: :dense)