iOS App Development: Core ML
1h 40mIntermediate2017-11-14
Authors
Brian Advent
Software Developer, Educator, Mathematician
Course details
With iOS 11 and macOS High Sierra, Apple introduced the Core ML framework. This put the integration of machine learning within the grasp of Apple developers, and ushered in a host of new opportunities for developers to come up with the next big thing or enhance the quality of their mobile and desktop applications. This course provides an introduction to the Core ML framework, and the advantages of using machine learning models, computer vision, and natural language processing in modern apps. Learn about different features and use cases for the Core ML and Vision frameworks, as well as the natural language processing classes. Plus, the course walks through the development of sample apps that leverage different machine learning features.
Learning objectives
What are machine learning, Core ML, Vision, and NLP?
Adding a machine learning model to a project
Getting predictions from machine learning models
Converting existing machine learning models for Core ML
Classifying images and detecting objects with Vision and Core ML
Analyzing natural language text with NSLinguisticTagger
Learning objectives
What are machine learning, Core ML, Vision, and NLP?
Adding a machine learning model to a project
Getting predictions from machine learning models
Converting existing machine learning models for Core ML
Classifying images and detecting objects with Vision and Core ML
Analyzing natural language text with NSLinguisticTagger
Skills covered
iOS DevelopmentiOSMobile DevelopmentAppleDeep Dive (X:Y)
Concepts
0. Introduction
- 01 - Welcome
- 02 - What you should know
- 03 - Using the exercise files
1. Machine Learning Overview
- 04 - What is machine learning
- 05 - What is Core ML
- 06 - What is Vision and NLP
2. Core ML - Basics and First Project
- 07 - Existing Core ML models
- 08 - From name to gender - Your first ML app
- 09 - Add a model and prepare your inputs
- 10 - Get your first predictions
3. Convert Models
- 11 - Prepare a virtual Python environment
- 12 - Install Python dependencies
- 13 - Convert an existing machine learning model for Core ML
4. The Vision Framework
- 14 - Classify images and detect rectangles
- 15 - A quick project tour and AVFoundation
- 16 - Set up vision and rectangle detection
- 17 - Draw rectangles on the camera image
- 18 - A Core ML model for image classification
- 19 - Let's classify what the camera sees
5. Natural Language Processing (NLP)
- 20 - See NSLinguisticTagger in action
- 21 - A diary app - Search without NLP
- 22 - An NLP enhanced search
- 23 - Lemmatization in detail
- 24 - Implement an NLP enhanced filter logic
Conclusion
- 25 - Next steps