Merge pull request aws#385 from jmikko/master

Fair Linear Learner

Author: djarpin

advanced_functionality/README.md

@@ -14,4 +14,4 @@ These examples that showcase unique functionality available in Amazon SageMaker.
 - [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](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 MXNet Model](mxnet_mnist_byom) shows how to bring a model trained anywhere using MXNet into Amazon SageMaker
-- [Bring Your Own TensorFlow Model](tensorflow_iris_byom) shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker
+- [Bring Your Own TensorFlow Model](tensorflow_iris_byom) shows how to bring a model trained anywhere using TensorFlow into Amazon SageMaker

introduction_to_applying_machine_learning/README.md

@@ -11,5 +11,5 @@ These examples provide a gentle introduction to machine learning concepts as the
 - [Ensembling](ensemble_modeling) predicts income using two Amazon SageMaker models to show the advantages in ensembling.
 - [Video Game Sales](video_game_sales) develops a binary prediction model for the success of video games based on review scores.
 - [MXNet Gluon Recommender System](gluon_recommender_system) uses neural network embeddings for non-linear matrix factorization to predict user movie ratings on Amazon digital reviews.
+- [Fair Linear Learner](fair_linear_learner) is an example of an effective way to create fair linear models with respect to sensitive features.
 - [Population Segmentation of US Census Data using PCA and Kmeans](US-census_population_segmentation_PCA_Kmeans) analyzes US census data and reduces dimensionality using PCA then clusters US counties using KMeans to identify segments of similar counties.
-