diff --git a/README.md b/README.md
index 4a5e7bf6e..97e348b43 100644
--- a/README.md
+++ b/README.md
@@ -6,11 +6,11 @@ DMLC/XGBoost
 An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version.
 It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data
 
-Checkout our [Comitters and Contributors](CONTRIBUTORS.md) who keep make xgboost better.
+Check out our [Committers and Contributors](CONTRIBUTORS.md) who help make xgboost better.
 
-Documentations: [Documentation of dmlc/xgboost](doc/README.md)
+Documentation: [Documentation of dmlc/xgboost](doc/README.md)
 
-Issues Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion)
+Issue Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion)
 
 Please join [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) to ask questions and share your experience on xgboost.
   - Use issue tracker for bug reports, feature requests etc.
@@ -27,16 +27,16 @@ XGBoost is part of [Distributed Machine Learning Common](http://dmlc.github.io/)
 What's New
 ==========
 * XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance)
-  - Checkout the winning solution at [Highlight links](doc/README.md#highlight-links)
+  - Check out the winning solution at [Highlight links](doc/README.md#highlight-links)
 * XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04)
 * XGBoost helps three champion teams to win [WWW2015  Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing)
-  - Checkout the winning solution at [Highlight links](doc/README.md#highlight-links)
+  - Check out the winning solution at [Highlight links](doc/README.md#highlight-links)
 * [External Memory Version](doc/external_memory.md)
 
 Contributing to XGBoost
 =========
-XGBoost has been developed and used by a group of active community. Everyone is more than welcomed to is a great way to make the project better and more accessible to more users.
-* Checkout [Feature Wish List](https://github.com/dmlc/xgboost/labels/Wish-List) to see what can be improved, or open an issue if you want something.
+XGBoost has been developed and used by a group of active community members. Everyone is more than welcome to contribute. It is a way to make the project better and more accessible to more users.
+* Check out [Feature Wish List](https://github.com/dmlc/xgboost/labels/Wish-List) to see what can be improved, or open an issue if you want something.
 * Contribute to the [documents and examples](https://github.com/dmlc/xgboost/blob/master/doc/) to share your experience with other users.
 * Please add your name to [CONTRIBUTORS.md](CONTRIBUTORS.md) after your patch has been merged.
 
@@ -66,5 +66,5 @@ Version
 
 XGBoost in Graphlab Create
 ==========================
-* XGBoost is adopted as part of boosted tree toolkit in Graphlab Create (GLC). Graphlab Create is a powerful python toolkit that allows you to data manipulation, graph processing, hyper-parameter search, and visualization of TeraBytes scale data in one framework. Try the Graphlab Create in http://graphlab.com/products/create/quick-start-guide.html
-* Nice blogpost by Jay Gu using GLC boosted tree to solve kaggle bike sharing challenge: http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand
+* XGBoost is adopted as part of boosted tree toolkit in Graphlab Create (GLC). Graphlab Create is a powerful python toolkit that allows you to do data manipulation, graph processing, hyper-parameter search, and visualization of TeraBytes scale data in one framework. Try the Graphlab Create in http://graphlab.com/products/create/quick-start-guide.html
+* Nice blogpost by Jay Gu about using GLC boosted tree to solve kaggle bike sharing challenge: http://blog.graphlab.com/using-gradient-boosted-trees-to-predict-bike-sharing-demand