- [Instructor] We can now prepare to make … a call to the service, and so we're going to create … a URL variable to contain the service endpoint … and that's just going to be a string, … and we'll go back out to the Watson portal … to get that endpoint. … So if we go up to manage, we want to go … and open Watson Studio, and in Watson Studio we'll select … our BudgetVotePrediction project … and look at our deployments, and our BudgetVoteService. … And we'll look at the implementation … and here we can see our scoring endpoints … so that's what we want, so I'm going to put that … in the clipboard and go back to Visual Studio … and paste that in. … And then we're going to create a HTTPWebRequest … and we'll call that request, and that's going to be equal to … and we'll direct cast that as a web request. … It will be WebRequest.Create … and we're going to pass in that URL variable. … The next step is to add the headers for the request … and we're going to use the token as a bearer token. …
- 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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