[DSS Tutorial] Updated the readme.

dss-2016/README.md

@@ -4,17 +4,32 @@ This directory contains demo notebooks used for the collection of **machine
 learning tutorials** at the [Data Science Summit
 2016](https://conf.turi.com/2016/us/). 
 
-The course is designed to introduce machine learning via real applications like 
-- recommender systems
-- object dection with deep learning
-- predicting customer churn
-- lead scoring 
+The tutorials introduce machine learning via real applications like:
+- recommender systems [Event
+  Page](https://turi.com/events/training/2016-dss-personalization-tutorial.html)
+- object dection with deep learning [Event
+  Page](https://turi.com/events/training/2016-dss-image-apps-tutorial.html)
+- predicting customer churn [Event
+  Page](https://turi.com/events/training/2016-dss-customer-intelligence-tutorial.html)
+- lead scoring [Event
+  Page](https://turi.com/events/training/2016-dss-customer-intelligence-tutorial.html)
 
 ## Setup Instructions
 
-You can browse the notebooks using Github's notebook viewer, but please note
-that some images may not be rendered correctly. Follow these steps to set up
-and run the notebooks on your own machine.
+You  can browse the notebooks using Github's notebook viewer, but please note
+that some images may not be rendered correctly. 
+
+Follow these steps to set up and run the notebooks on your own machine.
+
+- [Download](https://turi.com/download/) GraphLab Create v2.0.1 and then follow
+  instructions to [install](https://turi.com/download/install.html).
+
+- Download and unzip the datasets
+  [here](https://s3-us-west-2.amazonaws.com/turi-tutorials/TURI.zip).
+
+- [Install Jupyter
+  notebook](http://jupyter.readthedocs.org/en/latest/install.html) (needed only
+  if you install GraphLab Create via command line)
 
 ## Handy references