Learn how to apply the power of machine learning to mobile app development, using platforms such as IBM Watson, Microsoft Azure Cognitive Services, and Apple Core ML.
- [Instructor] Machine learning is used everywhere today from self-driving cars to personal assistants like Cortana, Alexa and Siri, and to security technologies like facial recognition. Mobile developers are increasingly being asked to implement machine learning technologies into their apps. For many of them, this is new territory. In this course, we'll look at how machine learning relates to the field of AI in a way that's geared toward application developers. I will demonstrate different machine learning products like IBM Watson and cognitive services from Microsoft Azure and show you how to use them to create natural language recognition, visual recognition, and statistical models for use in a Xamarin application. Hi, I'm Kevin Ford, and I've been helping clients make great software for somewhere north of 20 years. So join me and my LinkedIn learning course about machine learning and how mobile developers can use these technologies in their apps.
- 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.