- [Instructor] No we are ready to hook up a call … to our custom model to predicting House votes from 1984. … We deployed the model as a web service. … We can contact it using the REST API. … The code we need to implement … is within the House vote prediction view model. … And I'm going to scroll down a bit. … And the method we want to implement … is GetYesVoteChanceAsync. … This method returns a double, … which is a chance the person will vote yes … based on their party, … and the other votes they made. … To call the web service, … we must first call to get a token. … We'll create a variable to hold the URL … for the token service. … So it will be var tokenURL equals and this will be a string. … And to get that tokenURL, … we're going to go over to the IBM Portal, … and here's a list of our services. … We're going to open up our machine learning service … called LinkedInBudgetVotes. … Go to Service Credentials, … and click on view credentials, … and at the bottom we'll see our URL, …
- 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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