Adds auto_ml and machineJS- automated machine learning 
diff --git a/README.md b/README.md
index 2e8fefcc..3588d80c 100644
--- a/README.md
+++ b/README.md
@@ -438,6 +438,7 @@ For a list of free-to-attend meetups and local events, go [here](https://github.
 * [LDA.js](https://github.com/primaryobjects/lda) - LDA topic modeling for node.js
 * [Learning.js](https://github.com/yandongliu/learningjs) - Javascript implementation of logistic regression/c4.5 decision tree
 * [Machine Learning](http://joonku.com/project/machine_learning) - Machine learning library for Node.js
+* [machineJS](https://github.com/ClimbsRocks/machineJS) - Automated machine learning, data formatting, ensembling, and hyperparameter optimization for competitions and exploration- just give it a .csv file!
 * [mil-tokyo](https://github.com/mil-tokyo) - List of several machine learning libraries
 * [Node-SVM](https://github.com/nicolaspanel/node-svm) - Support Vector Machine for nodejs
 * [Brain](https://github.com/harthur/brain) - Neural networks in JavaScript **[Deprecated]**
@@ -760,6 +761,7 @@ on MNIST digits[DEEP LEARNING]
 
 <a name="python-general-purpose" />
 #### General-Purpose Machine Learning
+* [auto_ml](https://github.com/ClimbsRocks/auto_ml) - Automated machine learning pipelines for analytics and production. Handles some standard feature engineering, feature selection, model selection, model tuning, ensembling, and advanced scoring, in addition to logging output for analysts trying to understand their datasets. 
 * [machine learning](https://github.com/jeff1evesque/machine-learning) - automated build consisting of a [web-interface](https://github.com/jeff1evesque/machine-learning#web-interface), and set of [programmatic-interface](https://github.com/jeff1evesque/machine-learning#programmatic-interface) API, for support vector machines.  Corresponding dataset(s) are stored into a SQL database, then generated model(s) used for prediction(s), are stored into a NoSQL datastore.
 * [XGBoost](https://github.com/dmlc/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

Thanks for maintaining this project! I already found a few new projects on here I want to add into auto_ml. 
