1. Getting Started with Microsoft Azure Machine Learning
Buck Woody | Senior Technical Specialist, Microsoft
Scott Klein | Senior Technical Evangelist, Microsoft
Seayoung Rhee | Senior Technical Product Manager, Microsoft

2. Meet Buck Woody | ‏@BuckWoody
Senior Technical Specialist, Microsoft Corporation
Instructor, University of Washington, Data Science Board
Over 30 years of data and teaching experience
Frequent Speaker at various conferences worldwide

3. Meet Seayoung Rhee | ‏@seayoungrhee
Senior Technical Product Manager, Microsoft
Machine Learning and Advanced Analytics technical marketing

4. Meet Scott Klein | ‏@SQLScott
Senior Technical Evangelist, Microsoft Corporation
Frequent Speaker at various conferences worldwide

5. Course Topics Getting Started with AzureML
01 | An Introduction to Machine Learning and AzureML Studio
02 | Designing a Recommender Solution with AzureML
03 | Monetizing your AzureML Application with the Microsoft Azure Marketplace
04 | AzureML API Services and Extensibility Scenarios

6. Setting Expectations Target Audience
Data Professional
Developer
Business Intelligence Professional
Suggested Prerequisites/Supporting Material
Programming
Probability / Statistics
Calculus
Linear Algebra

7. Join the MVA Community! Microsoft Virtual Academy
Free online learning tailored for IT Pros and Developers
Over 1M registered users
Up-to-date, relevant training on variety of Microsoft products
“Earn while you learn!”
Get 50 MVA Points for this event!
Visit
Enter this code: PowerJump1 (expires 8/15/2013)

8. 01 | An Introduction to Machine Learning and AzureML Studio
Buck Woody | Senior Technical Specialist, Microsoft
Seayoung Rhee | Senior Technical Product Manager, Microsoft

9. Module Overview Machine Learning Overview AzureML Strengths
Microsoft Azure Overview, Getting an Account and Using Storage Accounts
Setting up an AzureML Workspace
Exploring AzureML Studio

10. Machine Learning / Predictive Analytics
Server & Tools Business
12/9/2019
Machine Learning / Predictive Analytics
Machine learning & predictive analytics are core capabilities that are needed throughout your business
Vision Analytics
Recommenda-tion engines
Advertising analysis
Weather forecasting for business planning
Social network analysis
Legal discovery and document archiving
Pricing analysis
Fraud detection
Churn analysis
Equipment monitoring
Location-based tracking and services
Personalized Insurance
© 2012 Microsoft Corporation. All rights reserved. Microsoft, Windows, and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

11. Machine Learning Overview
Formal definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E” - Tom M. Mitchell
Another definition: “The goal of machine learning is to program computers to use example data or past experience to solve a given problem.” – Introduction to Machine Learning, 2nd Edition, MIT Press
ML often involves two primary techniques:
Supervised Learning: Finding the mapping between inputs and outputs using correct values to “train” a model
Unsupervised Learning: Finding patterns in the input data (similar to Density Estimates in Statistics)

12. Machine Learning A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Data:
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Rules, or Algorithms:
about, Learning, language – Spelling and sounding builds words
Learning about language. – Words build sentences
Learning, or Abstraction:
Any new understanding proceeds from previous knowledge.

13. Supervised Learning
Used when you want to predict unknown answers from answers you already have – requires data which shows the answers you can get now
Data is divided into two parts: the data you will use to “teach” the system (data set), and the data you will use to see if the computer’s algorithms are accurate (test set)
After you select and clean the data, you select data points that show the right relationships in the data. The answers are “labels”, the categories/columns/attributes are “features” and the values are…values.
Then you select an algorithm to compute the outcome. (Often you choose more than one)
You run the program on the data set, and check to see if you got the right answer from the test set.
Once you perform the experiment, you select the best model. This is the final output – the model is then used against more data to get the answers you need

14. Supervised Learning
Car
Not Car

15. Unsupervised Learning
Used when you want to find unknown answers – mostly groupings - directly from data
No simple way to evaluate accuracy of what you learn
Evaluates more vectors, groups into sets or classifications
Start with the data
Apply algorithm
Evaluate groups

16. Unsupervised Learning
Example example A Example 2 example B Example 3 example C
example A
example B
example C
Example 1
Example 2
Example 3

17. AzureML Why AzureML? Setting up a Microsoft Azure Account
Setting up a Storage Account
Loading Data
Setting up an AzureML Workspace
Accessing AzureML Studio
AzureML Studio Tour