Merge branch 'master' of https://github.com/dato-code/tutorials

Conflicts:
	dss-2016/README.md
	dss-2016/churn_prediction/churn-tutorial.ipynb

README.md

@@ -1,14 +1,14 @@
-# Dato Tutorials
+# Turi Tutorials
 
-This repository contains materials for demos, tutorials, and talks by Dato Inc.
+This repository contains materials for demos, tutorials, and talks by Turi.
 You can browse the notebooks using Github's own notebook viewer. Note that some
 images may not be rendered correctly. 
 
 If you'd like to run it, you may register for GraphLab Create
-(https://dato.com/download/), then follow instructions to install.
+(https://turi.com/download/), then follow instructions to install.
 
-- GraphLab Create User Guide: http://dato.com/learn/userguide
-- GraphLab Forum: http://forum.dato.com/categories/graphlab-create
+- GraphLab Create User Guide: https://turi.com/learn/userguide
+- GraphLab Forum: http://forum.turi.com/categories/graphlab-create
 
 # Events
 

dss-2016/README.md

@@ -2,7 +2,7 @@
 
 This directory contains demo notebooks used for the collection of **machine
 learning tutorials** at the [Data Science Summit
-2016](http://conf.dato.com/2016/us/). 
+2016](https://conf.turi.com/2016/us/). 
 
 The course is designed to introduce machine learning via real applications like 
 - recommender systems
@@ -18,5 +18,5 @@ and run the notebooks on your own machine.
 
 ## Handy references
 
-- [GraphLab Create User Guide](http://dato.com/learn/userguide)
-- [GraphLab Forum](http://forum.dato.com/categories/graphlab-create)
+- [GraphLab Create User Guide](https://turi.com/learn/userguide)
+- [GraphLab Forum](http://forum.turi.com/categories/graphlab-create)

dss-2016/recommendation-systems/README.md

@@ -0,0 +1,23 @@
+# Data Science Summit 2016 
+
+This directory contains demo notebooks used for the "Introduction to Recommender Systems", the second session of **Machine Learning 101**.
+
+In this session we 
+
+- give an introduction to recommendation systems, 
+- show how easy it is to get started
+- provide examples and slides
+
+Along the way, we also cover feature engineering and deploying machine learning models as a predictive service.
+
+## Setup Instructions
+
+You can browse the notebooks using Github IPython notebook viewer. Note that some images may not be rendered correctly. If you'd like to run it, follow these steps to set up your machine.
+
+- [Download](https://turi.com/download/) GraphLab Create and then follow instructions to [install](https://turi.com/download/install.html).
+- Download and unzip the datasets 
+
+## Handy references
+
+- [GraphLab Create User Guide](https://turi.com/learn/userguide)
+- [Turi Forum](http://forum.turi.com/categories/graphlab-create)