Merge pull request aws#117 from awslabs/arpin_readme_edits

Fixed: README grammar edits

Author: djarpin

README.md

@@ -39,7 +39,7 @@ These examples provide more thorough mathematical treatment on a select group of
 These examples that showcase unique functionality available in Amazon SageMaker.  They cover a broad range of topics and will utilize a variety of methods, but aim to provide the user with sufficient insight or inspiration to develop within Amazon SageMaker.
 
 - [Data Distribution Types](advanced_functionality/data_distribution_types) showcases the difference between two methods for sending data from S3 to Amazon SageMaker Training instances.  This has particular implication for scalability and accuracy of distributed training.
-- [Encrypting Your Data](advanced_functionality/handling_kms_encrypted_data) shows how to use Server Side KMS encrypted data with Amazon SageMaker training works. The IAM role used for S3 access needs to have permissions to encrypt and decrypt data with the KMS key.
+- [Encrypting Your Data](advanced_functionality/handling_kms_encrypted_data) shows how to use Server Side KMS encrypted data with Amazon SageMaker training. The IAM role used for S3 access needs to have permissions to encrypt and decrypt data with the KMS key.
 - [Using Parquet Data](advanced_functionality/parquet_to_recordio_protobuf) shows how to bring [Parquet](https://parquet.apache.org/) data sitting in S3 into an Amazon SageMaker Notebook and convert it into the recordIO-protobuf format that many SageMaker algorithms consume. 
 - [Connecting to Redshift](advanced_functionality/working_with_redshift_data) demonstrates how to copy data from Redshift to S3 and vice-versa without leaving Amazon SageMaker Notebooks.
 - [Bring Your Own XGBoost Model](advanced_functionality/xgboost_bring_your_own_model) shows how to use Amazon SageMaker Algorithms containers to bring a pre-trained model to a realtime hosted endpoint without ever needing to think about REST APIs.
@@ -66,9 +66,8 @@ These examples focus on the Amazon SageMaker Python SDK which allows you to writ
 
 These Amazon SageMaker examples fully illustrate a concept, but may require some additional configuration on the users part to complete.
 
-- [Bring Your Own MXNet Model](under_development/tensorflow_iris_byom) shows how to bring a model trained anywhere using MXNet into Amazon SageMaker
+- [Bring Your Own MXNet Model](under_development/mxnet_mnist_byom) shows how to bring a model trained anywhere using MXNet into Amazon SageMaker
 - [Bring Your Own TensorFlow Model](under_development/tensorflow_iris_byom) shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker
-- [Ensembling Multiple Models](under_development/modeling) creates two different models for prediction, hosts them independently and shows how their outputs can be combined for better accuracy than either one alone.
 
 ## FAQ
 
@@ -78,4 +77,4 @@ These Amazon SageMaker examples fully illustrate a concept, but may require some
 
 *How do I contribute my own example notebook?*
 
-- Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from and external source.  Please bear with us in the short-term if pull requests take longer than expected or are closed.
+- Although we're extremely excited to receive contributions from the community, we're still working on the best mechanism to take in examples from external sources.  Please bear with us in the short-term if pull requests take longer than expected or are closed.

advanced_functionality/README.md

@@ -5,11 +5,11 @@
 These examples that showcase unique functionality available in Amazon SageMaker.  They cover a broad range of topics and will utilize a variety of methods, but aim to provide the user with sufficient insight or inspiration to develop within Amazon SageMaker.
 
 - [Data Distribution Types](data_distribution_types) showcases the difference between two methods for sending data from S3 to Amazon SageMaker Training instances.  This has particular implication for scalability and accuracy of distributed training.
-- [Encrypting Your Data](handling_kms_encrypted_data) shows how to use Server Side KMS encrypted data with Amazon SageMaker training works. The IAM role used for S3 access needs to have permissions to encrypt and decrypt data with the KMS key.
+- [Encrypting Your Data](handling_kms_encrypted_data) shows how to use Server Side KMS encrypted data with Amazon SageMaker training. The IAM role used for S3 access needs to have permissions to encrypt and decrypt data with the KMS key.
 - [Using Parquet Data](parquet_to_recordio_protobuf) shows how to bring [Parquet](https://parquet.apache.org/) data sitting in S3 into an Amazon SageMaker Notebook and convert it into the recordIO-protobuf format that many SageMaker algorithms consume. 
 - [Connecting to Redshift](working_with_redshift_data) demonstrates how to copy data from Redshift to S3 and vice-versa without leaving Amazon SageMaker Notebooks.
 - [Bring Your Own XGBoost Model](xgboost_bring_your_own_model) shows how to use Amazon SageMaker Algorithms containers to bring a pre-trained model to a realtime hosted endpoint without ever needing to think about REST APIs.
 - [Bring Your Own k-means Model](kmeans_bring_your_own_model) shows how to take a model that's been fit elsewhere and use Amazon SageMaker Algorithms containers to host it.
 - [Installing the R Kernel](install_r_kernel) shows how to install the R kernel into an Amazon SageMaker Notebook Instance.
 - [Bring Your Own R Algorithm](r_bring_your_own) shows how to bring your own algorithm container to Amazon SageMaker using the R language.
-- [Bring Your Own scikit Algorithm](advanced_functionality/scikit_bring_your_own) provides a detailed walkthrough on how to package a scikit learn algorithm for training and production-ready hosting.
+- [Bring Your Own scikit Algorithm](scikit_bring_your_own) provides a detailed walkthrough on how to package a scikit learn algorithm for training and production-ready hosting.

introduction_to_applying_machine_learning/README.md

@@ -8,3 +8,4 @@ These examples provide a gentle introduction to machine learning concepts as the
 - [Predicting Customer Churn](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](linear_time_series_forecast) generates a forecast for topline product demand using Amazon SageMaker's Linear Learner algorithm.
 - [Cancer Prediction](breast_cancer_prediction) predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner.
+- [Ensembling](ensemble_modeling) predicts income using two Amazon SageMaker models to show the advantages in ensembling.

sagemaker-python-sdk/README.md

@@ -4,12 +4,12 @@
 
 These examples focus on the Amazon SageMaker Python SDK which allows you to write idiomatic TensorFlow or MXNet and then train or host in pre-built containers.
 
-- [cifar 10 with MXNet Gluon](sagemaker-python-sdk/mxnet_gluon_cifar10)
-- [MNIST with MXNet Gluon](sagemaker-python-sdk/mxnet_gluon_mnist)
-- [MNIST with MXNet](sagemaker-python-sdk/mxnet_mnist)
-- [Sentiment Analysis with MXNet Gluon](sagemaker-python-sdk/mxnet_gluon_sentiment)
-- [TensorFlow Neural Networks with Layers](sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_layers)
-- [TensorFlow Networks with Keras](sagemaker-python-sdk/tensorflow_abalone_age_predictor_using_keras)
-- [Introduction to Estimators in TensorFlow](sagemaker-python-sdk/tensorflow_iris_dnn_classifier_using_estimators)
-- [TensorFlow and TensorBoard](sagemaker-python-sdk/tensorflow_resnet_cifar10_with_tensorboard)
-- [Distributed TensorFlow)(sagemaker-python-sdk/tensorflow_distributed_mnist)
+- [cifar 10 with MXNet Gluon](mxnet_gluon_cifar10)
+- [MNIST with MXNet Gluon](mxnet_gluon_mnist)
+- [MNIST with MXNet](mxnet_mnist)
+- [Sentiment Analysis with MXNet Gluon](mxnet_gluon_sentiment)
+- [TensorFlow Neural Networks with Layers](tensorflow_abalone_age_predictor_using_layers)
+- [TensorFlow Networks with Keras](tensorflow_abalone_age_predictor_using_keras)
+- [Introduction to Estimators in TensorFlow](tensorflow_iris_dnn_classifier_using_estimators)
+- [TensorFlow and TensorBoard](tensorflow_resnet_cifar10_with_tensorboard)
+- [Distributed TensorFlow](tensorflow_distributed_mnist)