Getting started
  • About Your Instructor and Course Overview
  • About Machine Learning
  • Activity: Playing with Machine Learning Style Transfer
Optional - iOS Fundamentals
  • About this section - start iOS
  • Download and install xcode for iOS 11
  • Get the iOS developer license
  • How to use a MAC on Windows PC or Linux
  • How to install iOS 11 on your iPhone or iPad
  • Use the Xcode interface
  • Xcode configuration files
Optional - Machine Learning Concepts
  • About this section - intro to ML
  • What is an Artificial Neuron - Neural Network
  • Parts of an Artificial Neural Network
  • Explanation - Convolutional Neural Network
  • Recurrent Neural Networks basics RNNs
iOS Machine Learning With Photos
  • About this section - coreML with Photos
  • Demo of project using coreML on photos
  • About ML model and Neural Networks
  • Project: Create the xcode project
  • Project: How to add ML models to xcode projects
  • Project: How to get pre-made ML models for iOS
  • Project: How to use ML models with images (part 1)
  • Project: How to use ML models with images (part 2)
  • Project: Programming the VN request callback method
  • Testing different ML models
  • Exercise: Models with Images input
  • Solution: Models with Images input
  • Basics of Machine Learning for iOS
  • Summary: coreML Vision with Photos
coreML All about custom models
  • About this section - model conversion
  • Project: Finding custom ML models
  • Project: Converting ML models get Anaconda IDE
  • Installing Python libraries for core ML
  • Installing Caffe tools for core ML conversion
  • Project: Converting scikit model to core ml mlmodel format
  • Working with Custom Models
CoreML with Data Set models
  • Introduction to Working with Data sets
  • Project: Create xcode project and add iris model
  • Project: ML dataset project User Interface
  • Project: Properties and picker delegate methods
  • Project: Pickerview data source methods
  • Project: Coding prediction for data sets
  • Project: Code improvements
  • Important data set models information
  • Working with Data Sets
Project: coreML with Video Camera
  • About CoreML with Video Camera
  • Project: Create xcode project and add VGG16 model
  • Project: Building the user interface
  • Project: Video Stream variables setup
  • Project: Program camera feed
  • Project: Capture image from video stream for ML model
  • Project: Programming the ML prediction launch
  • Project: Processing the ML model output
  • Testing the live camera feed with VGG model
END: iOS coreML fundamentals
  • Congratulations
Optional - Going the extra mile
  • Adding converted model metadata
  • Get a PixelBuffer from a UIImage
  • UIImage PixelBuffer extension (part 1)
  • UIImage PixelBuffer extension (part 2)
  • coreML prediction using UIImage PixelBuffer
Optional - Numerous Model Conversions
  • About model conversion types
  • Caffe - Get a Caffe ML model with weights and labels
  • CoreML tools conversion code with Caffe
  • Exporting Caffe model to mlmodel format
  • Caffe - Using the Caffe model with iOS
  • Keras - Load Save Keras models and convert to mlmodel
  • Vision Image Request parameter options
Advanced Vision Techniques: Face Detection
  • Introduction to advanced ML with Vision
  • Project: Create the user interface in storyboard
  • Project: Coding the Photo selection
  • Project: Coding Face Detection
  • Activity: Face Detection
  • Activity Solution: Face Detection
Optional - Advanced Face Features Detection
  • About Advanced Face Feature Recognition
  • Locate face position and area (part 1 of 2)
  • Locate face position and area (part 2 of 2)
  • Code to detect Face features eyes nose lips
  • face features part 2
  • face features part 3
  • face features part 4
  • Activity - draw all face features in blue
  • Activity Solution
Advanced Text Detection Techniques
  • About Project: Text Detection
  • Project: text recog part 1
  • Project: text recog part 2
  • Project: text recog part 3
  • Activity: Text Recognition