Predicting the Futurewith Azure Machine Learning

PresenterPaul PraeConsultant, Slalom ConsultingB.A. in Cognitive Science with a Focused Foundation in Artificial IntelligenceB.S. in Computer Science with an Area of Emphasis in Artificial Intelligence

@Praeducerwww.paulprae.com

What is Machine Learning?

The field of study that gives computers the ability to learn without being explicitly programmed.

MachineLearningAlgorithm

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OutputProgram

What is Machine Learning?

Unsupervised learning is the machine learning task of inferring a function to describe hidden structure from unlabeled data.

Supervised learning is the machine learning task of inferring a function from labeled training data.

Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.

Supervised Learning

Prediction with Supervised Learning

What is Predictive Analytics then?

Supervised learning is a technique for performing predictive analytics.

Supervised Learning vs. Predictive Analytics

Supervised learning is the machine learning task of inferring a function from labeled training data.

Predictive analytics encompasses a variety of statistical techniques that analyze current and historical facts to make predictions about future or otherwise unknown events.

Classification with a Decision Tree

The Machine Learning Process

What is Azure Machine Learning?• Azure Machine Learning provides tools for creating complete

predictive analytics solutions in the cloud: Quickly create, test, operationalize, and manage predictive models.

• Microsoft Azure Machine Learning Studio is a collaborative, interactive tool you can use to build, test, and deploy predictive analytics solutions on your data.

• You drag-and-drop datasets and analysis modules onto an interactive canvas, connecting them together to form an experiment, which you run in Machine Learning Studio.

Why Azure Machine Learning?

Minimal set-up costs with ability to easily scale compute/storage capacity; fewer barriers to entry

Easy to integrate data from various data sources

Users can collaborate in common toolset to build and train models using advanced algorithms

Easy to deploy trained models as consumable web services

Cloud-based

DataIntegration

CommonToolsetDeployment simplicity

Data Time

“How can I know, at the time of admission, if a new patient will successfully complete their substance abuse treatment plan?”

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EndQuestions and Feedback

PresenterPaul PraeConsultant, Slalom ConsultingB.A. in Cognitive Science with a Focused Foundation in Artificial IntelligenceB.S. in Computer Science with an Area of Emphasis in Artificial Intelligence

@Praeducerwww.paulprae.com

http://gotocon.com/dl/goto-aar-2014/slides/OscarNaim_AzureMachineLearningMachineLearningWithTheSimplicityAndProductivityOfTheCloud.pdf

http://www.slideshare.net/rjovic/azure-machine-learning-101

http://dilbert.com/strip/2013-02-02

https://en.wikipedia.org/wiki/Book:Machine_Learning_%E2%80%93_The_Complete_Guide

http://gotocon.com/dl/goto-aar-2014/slides/OscarNaim_AzureMachineLearningMachineLearningWithTheSimplicityAndProductivityOfTheCloud.pdf

https://azure.microsoft.com/en-us/documentation/articles/machine-learning-studio-overview-diagram/

“CSE 546 Data Mining Machine Learning” by Pedro Domingos www.cs.washington.edu/546

https://azure.microsoft.com/en-us/documentation/articles/machine-learning-what-is-machine-learning/

Microsoft Azure Essentials Azure Machine Learning By Jeff Barnes bit.ly/1omR6wt

http://www.icpsr.umich.edu/icpsrweb/ICPSR/series/00238

http://www.healthdata.gov/

References