1. Online Conference
June 17th and 18th 2015
WWW.COLLAB365.EVENTS
Empower Your Applications with
Azure Machine Learning
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David Walker
Tech Aspect
Email : dave@radicaldave.com
Twitter : @DavidWalker
Facebook :
LinkedIn :
Sitecore Practice Directory, Sitecore MVP
Over 20+ years exp, 75% as a Consultant
Certified Scrum Master, Scrum Developer
MCP in 2003, MCAD & MCSD in 2005
Former Senior App Dev at Microsoft
Former two-time Microsoft ASP.NET MVP
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Agenda
• What is Azure? This is Collab365 - You got this!
• What is Machine Learning?
• What is AzureML?
• DataMarket.Azure
• Application Integration
• API/Data Management
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POP QUIZ: What is Machine Learning?
“Field of study that gives computers the ability
to learn without being explicitly
programmed”.
Arthur Samuel – 1959, source Wikipedia
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Machine Learning / Predictive Analytics
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
Machine learning &
predictive analytics are core
capabilities that are needed
throughout your business
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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)
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Machine Learning
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.
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Supervised Learning1. Used when you want to predict unknown answers from answers you already
have – requires data which shows the answers you can get now
2. 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)
3. 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.
4. Then you select an algorithm to compute the outcome. (Often you choose
more than one)
5. You run the program on the data set, and check to see if you got the right
answer from the test set.
6. 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
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Unsupervised Learning1. Used when you want to find unknown answers – mostly groupings -
directly from data
2. No simple way to evaluate accuracy of what you learn
3. Evaluates more vectors, groups into sets or classifications
4. Start with the data
5. Apply algorithm
6. Evaluate groups
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Agenda
• What is Azure? This is Collab365 - You got this!
• What is Machine Learning?
• What is AzureML?
• Market Place
• Application Integration
• API/Data Management
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Why is AzureML so Awesome?
• Google was first with just a simple Prediction Service, but it
required a lot of thought/work in building appropriate data
sets
• AzureML is less restrictive on data sets and with a much
friendlier set of tools has made it so that anyone can do it –
no PhD required.
• Then, easily integrate it into your applications, processes –
even Excel.
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How can you use AzureML today?
• Search DataMarket.Azure.com for published
services/experiments
• Text Analytics – Sentiment
• Twitter Sentiment Analysis
• Lexicon Based Sentiment Analysis
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Create your own AzureML experiments?
• Set up a Microsoft Azure Account
• Set up a Storage Account
• Load Data
• Set up an AzureML Workspace
• Accessing AzureML Studio
• AzureML Studio Tour
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0 – The bar was closed
before they determined the
most efficient door to enter.
10 Data Scientist standing
outside a bar, how many enter?
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Agenda
• What is Azure? This is Collab365 - You got this!
• What is Machine Learning?
• What is AzureML?
• DataMarket.Azure
• Application Integration
• API/Data Management
20. WWW.COLLAB365.EVENTS
Agenda
• What is Azure? This is Collab365 - You got this!
• What is Machine Learning?
• What is AzureML?
• DataMarket.Azure
• Application Integration
• API/Data Management
22. WWW.COLLAB365.EVENTS
Agenda
• What is Azure? This is Collab365 - You got this!
• What is Machine Learning?
• What is AzureML?
• DataMarket.Azure
• Application Integration
• API/Data Management