- [Instructor] The last thing we want to hook up … is our custom model. … Now we didn't have a NuGet package to work with, … so instead we're going to need to call … the rest API directly. … So we're going to close up the visual recognition view model … and save it if it needs to be saved … and go to the house vote prediction view model. … And we're going to look for a method called … get yes vote chance A sync. … And here it is, right now it just returns zero, … being a zero percent chance for a yes vote. … Now instead, we want to return the value of the service. … So to start with, we're going to set up … a variable for the encoding. … And that's going to be equal to ASCII. … And now we're going to want a URL for the end point … so we're going to go var, URL, and that equals, … and it's going to be a string. … And we can find this information inside … our predictive experiment in Azure Machine Learning Studio. … Going to go to my experiments, … and close up any windows that show up …
- Defining machine learning
- Training a machine learning model
- Comparing machine learning frameworks
- Using IBM Watson for mobile machine learning
- Using Azure Machine Learning for speech and image recognition
- Training Core ML models
- Comparing client-side and server-side models
Skill Level Beginner
Xamarin and Android Studio: Material Designwith Kevin Ford1h 47m Intermediate
Machine Learning for iOS Developerswith Brian Advent1h 25m Advanced
1. Introduction to Machine Learning
2. Server Models: IBM Watson
3. Server Models: Azure Machine Learning
4. Client Models: Core ML
5. Understanding the Offerings
Next steps1m 40s
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