diff --git a/README.md b/README.md
index 60088a3..fa64faf 100644
--- a/README.md
+++ b/README.md
@@ -117,7 +117,7 @@ For a list of free machine learning books available for download, go [here](http
 * [sofia-ml](https://code.google.com/p/sofia-ml/) - Suite of fast incremental algorithms.
 * [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox
 * [Caffe](http://caffe.berkeleyvision.org)  - A deep learning framework developed with cleanliness, readability, and speed in mind. [DEEP LEARNING]
-* [CXXNET](https://github.com/antinucleon/cxxnet) - Yet another deep learning framework with less than 1000 lines core code
+* [CXXNET](https://github.com/antinucleon/cxxnet) - Yet another deep learning framework with less than 1000 lines core code [DEEP LEARNING]
 * [XGBoost](https://github.com/tqchen/xgboost) - A parallelized optimized general purpose gradient boosting library. 
 * [CUDA](https://code.google.com/p/cuda-convnet/) - This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING]
 * [Stan](http://mc-stan.org/) - A probabilistic programming language implementing full Bayesian statistical inference with Hamiltonian Monte Carlo sampling
@@ -538,6 +538,7 @@ on MNIST digits[DEEP LEARNING]
 
 <a name="python-general-purpose" />
 #### General-Purpose Machine Learning
+* [XGBoost](https://github.com/tqchen/xgboost) - Python bindings for eXtreme Gradient Boosting (Tree) Library
 
 * [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Book/iPython notebooks on Probabilistic Programming in Python
 * [Featureforge](https://github.com/machinalis/featureforge) A set of tools for creating and testing machine learning features, with a scikit-learn compatible API
@@ -809,6 +810,7 @@ Angle Regression
 * [SuperLearner](https://github.com/ecpolley/SuperLearner) and [subsemble](http://cran.r-project.org/web/packages/subsemble/index.html) - Multi-algorithm ensemble learning packages.
 * [Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)
 * [fpc](http://cran.r-project.org/web/packages/fpc/index.html) - fpc: Flexible procedures for clustering
+* [XGBoost.R](https://github.com/tqchen/xgboost/tree/master/R-package) - R binding for eXtreme Gradient Boosting (Tree) Library
 
 <a name="r-data-analysis" />
 #### Data Analysis / Data Visualization