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“Machine learning is a way of getting computers to know things when they see
them by producing for themselves the rules their programmers cannot specify. The machines do this with heavy-duty
statistical analysis of lots and lots of data.”
“Machine Learning: Field of study that
gives computers the ability to learn
without being explicitly programmed.”
Arthur Samuel (1959)
“A computer program is said to learn
from experience E with respect to some
task T and some performance measure
P, if its performance on T, as measured
by P, improves with experience E.”
Tom Mitchell (1998)
“A breakthrough in Machine Learning would be worth
ten Microsoft’s”Bill Gates
ML ExamplesFROM THE PRESS
Spam Filtering
Google/Bing Ad Targeting
Postal Service Mail Sorting
Cortana
Amazon/Netflix Recommendations
Credit Card Fraud Detection
Deep Blue/Watson
How-Old.net
BUSINESS APPS SMART APPS
Automated Workflow Routing
Automated Filing
User Suggestions
Customers Likely to Buy
Customers Likely to Leave
Product Pricing
Order Anomalies
Applied ML – Skills Needed BYOD
◦ Bring Your Own Development skills◦ REST
Data Processing/Cleansing◦ SQL/NoSQL◦ R and/or Python◦ Hadoop/HD Insight/Azure Stream Analytics
The Right Attitude◦ Persistence and confidence to understand a complex subject◦ Unbridled curiosity to explore and iterate and possibly fail◦ Creativity to find alternatives when you are blocked
ML Studio Workspace
Experiment - Modules◦ Training◦ Scoring
DataSet◦ Direct Upload – 10GB Limit◦ Reader – Azure Blob, Web Page, Odata, SQL Azure, Hive, etc◦ R or Python Module
Web Services
Demo1. Create a Training Experiment – Select a Model
2. Create a Scoring Experiment – Prep Selected Model for Runtime
3. Publish as a Web Service – Operationalize a Web Service
4. Consume a Web Service – Get Predictions from your App
Common ML ChallengesUNDERFITTING - BIAS OVERFITTING - VARIANCE
1. Add more features
2. Generate features
3. Evaluate training data
1. Reduce features – dimensionality reduction
2. Add more training data
3. Evaluate training data
Ecosystem Site/ML Studio/Docs: http://azure.microsoft.com/en-us/services/machine-learning/
Gallery: http://gallery.azureml.net/
Azure Marketplace: http://datamarket.azure.com/browse/data?category=machine-learning
Blog: http://blogs.technet.com/b/machinelearning/
Forum: https://social.msdn.microsoft.com/Forums/azure/en-US/home?forum=MachineLearning
Stack Overflow: http://stackoverflow.com/questions/tagged/azure-ml
Webinars: https://azureinfo.microsoft.com/BigDataAdvancedAnalyticsWebinars.html
Books Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes– Barga, Tok, and Fontama, Apress, 2014
Azure Machine Learning – Jeff Barnes, Microsoft Press, 2015
Data Science in the Cloud with Microsoft Azure Machine Learning and R – Stephen Elston, O’Reilly, 2015
Questions Contact Info:
@CAMCHENRY
http://cmchenry.com
http://www.linkedin.com/in/cmchenry
https://plus.google.com/+chrismchenry