From the ready-to-consume set of Azure Cognitive Services to the comprehensive set of tools for data scientists available in Azure Machine Learning Service, there are many ways to apply AI into your products and services.
I find that machine learning experiment’s results are always interesting and somewhat unexpected in certain cases. On this comparison, the feature ranking results of PFI are often different from the feature selection statistics that are utilized before a model is created. This is useful in many cases, especially when training “black-box” models where it is difficult to explain how the model characterizes the relationship between the features and the target variable.
Microsoft’s vision is to democratize AI and make it accessible and valuable to everyone. Join us to learn how to start building intelligence into your solutions with the Microsoft AI platform, including pre-trained AI services like Cognitive Services and Bot Framework, as well as deep learning tools like Azure Machine Learning.
In this post, App Dev Managers Edward Fry and Sheldon Ledbetter explorer the practical implications of Logical Regression and how we’re using to solve problems in systems via Machine Learning.
Logistic Regression. The very phrase is a mouthful. It’s easy to imagine it being used by actors to improve elocution or by math professors to punish wayward students.
Senior Application Development Manager, Justin Scott, launched a new podcast this month you will want to check out. AI exposed looks at the world of Artificial Intelligence– discussing some of the latest trends, interviewing industry experts, and having fun in the process.
Premier Support for Developers was part of the first ever Cortana Analytics Workshop earlier this month. Be sure and check out our own Brian Raymer discuss how an international customer can ingest millions of events per hour, exploit near-real-time analytics, and tackle true ‘Big Data’
Chandra Sekar, Senior Application Development Manager, shares how his simple exploration into Azure Machine Learning quickly produced an accurate predication of March Madness.
If you watched the Day 2 BUILD keynote you might have noticed Joseph Sirosh, CVP-Machine Learning, talking about an internal hackathon where competitors used Azure ML to predict the 2015 March Madness bracket.