- [Instructor] We can use the curl command … to send our CSV file to the service to be trained. … So, in the terminal window, … go and type in curl and minus I and minus minus and user. … And we'll give it an API key with a colon. … And we can grab the API key over on the service side. … If we go to manage, … and we can see the API key here. … And there's a copy button, … so we're going to click that and copy it to the clipboard. … Go over to our service and paste that in. … And now we'll specify what file to use. … And that will be training underscore data equals … and then we're going to use an at sign. … So the at sign is going to tell us … if we want to file out the device. … So we'll go to the path of where that is. … And it's on the desktop. … And under exercise file, we're going to use the backslash … because we have a space on the path. … And it's under IBM Watson Collateral. … And the name of the file is natural language trainer. … Now we're also going to give it some meta data. …
- 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
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.