Course Overview

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.

Course Circullum

Introduction

Machine learning in mobile apps
What you should know
Using the exercise files

1. Introduction to Machine Learning

What is machine learning?
Required concepts
Why does this matter for my app?
Training a model
Machine learning vs. deep learning
What can I do with machine learning?
Server-side vs. client-side ML
ML frameworks

2. Server Models: IBM Watson

Overview of Watson
Natural Language Understanding: Set up
Natural Language Understanding: Train the model
Visual Recognition: Set up
Visual Recognition: Train the model
Create a custom model
Train and deploy a custom model
Install client SDK package
Client tie to Natural Language
Client tie to Visual Recognition call setup
Client tie to Visual Recognition response
Client tie to custom model: Get an access token
Client tie to call custom model service
Client tie to get custom model response
Run the client app

3. Server Models: Azure Machine Learning

Azure Machine Learning overview
Language Understanding: Set up
Language Understanding: Intents
Language Understanding: Utterances
Custom Vision: Set up
Machine Learning Studio: Set up
Machine Learning Studio: Create model
Machine Learning Studio: Publish model
Install client SDK package
Client tie to LUIS
Client tie to Custom Vision model
Client tie to custom model
Client tie to custom model: Set up request
Client tie to custom model: Make the call
Run the clent app

4. Client Models: Core ML

Core ML overview
Core ML: Create Natural Language model
Core ML: Create Visual Recognition model
Client tie to Natural Language model
Client tie to Visual Recognition model
Client tie to Visual Recognition: Converting model
Run the client app

5. Understanding the Offerings

Different philosopies of the vendors
Why client-side model vs. server-side
When to use one or the other of these solutions

Conclusion

Next steps

Kevin Ford

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