- [Instructor] We're now going … to use Watson's image recognition service. … We start in the IBM Watson portal … and click on the hamburger menu. … And go down to Watson, … and browse all Watson's services. … From there we want to create a service … called Visual Recognition. … So I'm going to click on this box here … and create the Visual Recognition service. … And I'm going to change the name … to LinkedInVisualRecognition. … And I'm going to leave the region here as Dallas. … And I'm going to make sure … that we have the free Lite version selected. … And when I'm ready to go, I'm going to press Create. … And that's going to create a new service for us. … Out of the box, … Watson's Visual Recognition Service can detect … many common items and images. … It also has several built-in items … they refer to as classifiers. … A classifier is part of the model … that is trained to detect certain types of images … such as faces. … We're going to create a classifier of our own, … and that's what we're going to do now. …
- 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.