This presentation was done at the Azure Boot Camp 2017 in Singapore held on 23rd April 2017. This presentation covers an general introduction to machine learning and Azure Machine Learning Platform. To introduce the various features of the platform we make use of the Matchbox Recommender to build a collaborative filtering on the MovieLens dataset. We also obtain the state of art Normalized Discounted Cumulative Gain (NDCG) of 0.92 without supplementing any additional user data or movie tags. We just use the movie ratings as the base. I also show how to build an intuition for the recommendation systems using an Excel Worksheet and show how the latent factors in the recommendation systems are built and what the algorithm does to obtain an optimal solution. The link to the Excel Worksheet will be made available after the session is over.

1. Getting started with Azure Machine Learning
Setu Chokshi

2. What will we do
• What can machine learning do for me?
• How does it work?
• Lets develop some state of the art recommendation systems.
• Are my results good?
• Publishing your machine learning model.

3. Survey
• Participate with us and win exciting prizes
http://bit.ly/mygab
TweetTags: #GlobalAzure #GABSG

4. Stay till end 
SWAGs, PRIZES,TAKE-AWAYS
Xbox One System
Microsoft Azure IoT Starter Kit with Raspberry Pi 3
Raspberry Pi 3
Software Licenses and more...

5. This is an intro session.
Don’t wait till the end for
the questions. Just ask.

6. What can machine learning do for me?

7. What can machine learning do?
There are only 5 questions that machine
learning can help answer
Source: Data Science For Beginners - 5 Questions Data Science Answers by Brandon Rohrer

8. 1. Is this A or B?

9. 2. Is this weird?

10. 3. How much? How many?

11. 4. How is this organized?
How is this organized?

12. 5. What should I do now?

13. Machine Learning Process

14. How does it work?

15. How?
•Define & initialise a model
•Train model (process cases)
•Validate model
• …by scoring (making predictions) a test data set
and evaluating the results
•Use it: Explore or Deploy
• …visualise and study
• …deploy as a (web) service

16. Cheat Sheet
http://download.microsoft.com/download/A/6/1/A613E11E-8F9C-424A-B99D-65344785C288/microsoft-machine-learning-algorithm-cheat-sheet-v6.pdf

17. Collaborative Filtering using Matchbox
recommender

18. What is Collaborative Filtering?
Method of making automatic predictions (filtering)
By collecting preferences or tasters from many users (collaborating)
Assumption
If a Person A has the same taste/preferences/opinion as Person B, it is
more likely that Person A will have the same/similar opinion as Person B
on an issue X than a person chosen randomly

19. Matchbox Recommender
Instead lets build some intuition

20. Are my results good?
NDCG = Normalized Discounted Cumulative Gain

21. What is Collaborative Filtering?
Method of making automatic predictions (filtering)
By collecting preferences or tasters from many users (collaborating)
Assumption
If a Person A has the same taste/preferences/opinion as Person B, it is
more likely that Person A will have the same/similar opinion as Person B
on an issue X than a person chosen randomly

22. GetStartedwithMicrosoftAzure
Get theSDKs and command-line tools you need
http://azure.microsoft.com/en-us/downloads/
Learn more
http://azure.microsoft.com/
Likeusour
Facebook
page
Joinus@
meetup
group