Updated: README to include ensembling

Author: djarpin

README.md

@@ -12,6 +12,7 @@ These examples provide a gentle introduction to machine learning concepts as the
 - [Predicting Customer Churn](introduction_to_applying_machine_learning/xgboost_customer_churn) uses customer interaction and service usage data to find those most likely to churn, and then walks through the cost/benefit trade-offs of providing retention incentives.  This uses Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost) to create a highly predictive model.
 - [Time-series Forecasting](introduction_to_applying_machine_learning/linear_time_series_forecast) generates a forecast for topline product demand using Amazon SageMaker's Linear Learner algorithm.
 - [Cancer Prediction](introduction_to_applying_machine_learning/breast_cancer_prediction) predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner.
+- [Ensembling](introduction_to_applying_machine_learning/ensemble_modeling) predicts income using two Amazon SageMaker models to show the advantages in ensembling.
 
 ### Introduction to Amazon Algorithms