- Now we're going to use Watson to create a model from scratch. … In our exercise files, IBM Watson collateral folder, … we'll find a file called house votes 84 CSV. … This is a list of votes made by anonymized … US House of Representative members in 1984. … We're going to create a model that predicts a likelihood … someone voted for the budget based on party affiliation … and some of their prior votes … and here we are back in the IBM Cloud Dashboard … and we're going to create a new Watson Studio Instance. … So I'm going to go up to the hamburger menu … and scroll down to Watson, browse all Watson services, … and scroll down and click on Watson Studio … and we're going to call this LinkedIn Watson Studio. … Make sure the region stays at Dallas … and if we scroll down that we're using the free lite plan. … So we're going to hit create and create Watson Studio. … We also need a service to run our custom model. … So to do that, I'm once again going to go back up … to the hamburger menu, go to Watson, …
- 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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