- [Man] We set up our app to call … into a few Watson services. … One for language understanding, … one for image classification, … and one a totally custom model we made. … So, let's try our app out. … So, on the right we have our IOS simulator. … And I'm going to make sure that the IOS project … is selected as our start-up project … and we can right click on it, … and say set a start up. … And then I'm going to hit the run button. … And this is going to compile our app … and push it out to the simulator. … So, here we have a conversational UI. … On the bottom we have a text box we can type into … so let's find out what we can do. … So, we're going to say hi and press send. … Oh, hello and welcome. … So, it obviously understood … what we wanted to do. … So, I'll type in I don't know what to do. … Alright. … It comes back and tells me … what I can actually do. … So, one of the things that we can do … is evaluate the contents of an image. … So, let's go look at the contents of an image. …
- 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
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.