Getting Started with Microsoft Azure Machine Learning
- 2018-05-09 07:55 AM
If you're not a data scientist, but you're interested in data mining and predictive analytics, and you want to go beyond just reporting the numbers, check out this course on Azure Machine Learning (ML). ML is the inexpensive, easy-to-access, and powerful predictive analytics offering from Microsoft. In this demo-rich course, led by entertaining experts Buck Woody, Seayoung Rhee, and Scott Klein, get a real-world look at the different ways you can efficiently embed predictive analytics in your big data solutions, and explore best practices for analyzing trends and patterns. Find out about extending Azure ML using the Azure ML API services, and look at scenarios and methods for monetizing your ML application with Azure Marketplace.
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NOTE: To get the most out of this course, set up the Azure Machine Learning trial beforehand.
Instructor | Seayoung Rhee - Microsoft Senior Technical Product Manager; Buck Woody - Microsoft Senior Technical Specialist; Scott Klein - Microsoft Senior Technical Evangelist
Introduction to Machine Learning & Azure ML Studio
Learn the meaning of Machine Learning and its benefits, and get a quick introduction to basic techniques. See a demo of the Azure Machine Learning portal, and tour the ML Studio.
Designing a Predictive Analytics Solution with Azure ML
Watch an end-to-end scenario demo, and recreate a recommendation model from scratch in ML Studio. Learn about the process and flow of machine learning and what each module contributes, from start to finish.
Monetizing Your ML Application with Azure Marketplace
See a demo on publishing the finished app: begin with the two stage-process of publishing the app and then releasing it to production as a web service. Then, explore the process of registering as a publisher and of submitting the app to the Azure Data Marketplace for approval for monetization.
Azure ML API Services and Extensibility Scenarios
Learn to use the automatically generated C# code in the web service API, and run that code in Visual Studio. This code calls the API from the web service and returns the results, which can be used to embed Machine Learning technologies.
Video source via: MVA