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Complete iOS 11 Machine Learning Masterclass
About this course
03 About Machine Learning
About your instructor and course overview
03 Activity Use Machine Learning Style Transfer
Download all project files
Optional - iOS Fundamentals
About this section - start iOS
02 Download and install xcode for iOS 11
03 Get the iOS developer license
05 How to install iOS 11 on your iPhone or iPad
04 How to use a MAC on Windows PC or Linux
01 Use the Xcode interface
02 Xcode configuration files
Optional - Machine Learning Concepts
04 Recurrent Neural Networks basics RNNs
01 what is a neuron and neural network
02 Parts of an Artificial Neural Network
03 Explanation - Convolutional Neural Network
About this section - intro to ML
iOS Machine Learning With Photos
About this section - coreML with Photos
01 Demo of project using coreML on photos
02 About ML model and Neural Networks
03 Create the xcode project
04 How to get pre-made ML models for iOS
05 How to use ML models with images (part 1)
06 How to use ML models with images (part 2)
07 Programming the VN request callback method
08 Testing different ML models
09 Exercise with Models with Images input
10 solution with Models with Images input
coreML All about custom models
About this section - model conversion
01 Finding custom ML models
02 Converting ML models get Anaconda IDE
03 Installing Python libraries for core ML
04 Installing Caffe tools for core ML conversion
05 Converting scikit model to core ml mlmodel format
CoreML with Data Set models
01 intro to Working with Data sets
02 create xcode project and add iris model
03 ML dataset project User Interface
04 Properties and picker delegate methods
05 Pickerview data source methods
06 Coding prediction for data sets
07 Code improvements
08 Important data set imodels information
Project: coreML with Video Camera
00 About section ML Video
01 Demo Live Camera feed prediction
02 Create xcode project and add VGG16 model
03 Building the user interface
04 Video Stream variables setup
05 Program camera feed
06 Capture image from video stream for ML model
07 Programming the ML prediction launch
08 Processing the ML model output
09 Testing the live camera feed with VGG model
Section 8: END: iOS coreML fundamentals
Congratulations
Optional - Going the extra mile
Adding-converted-model-metadata
01-Get-a-PixelBuffer-from-a-UIImage
02-UIImage-PixelBuffer-extension-part-1
03-UIImage-PixelBuffer-extension-part-2
04-Using-the-UIImage-PixelBuffer-with-coreML-prediction
Optional - Numerous Model Conversions
Load-Save-Keras-models-and-convert-to-mlmodel
01-Vision-Image-Request-parameter-options
01-Get-a-Caffee-ML-model-with-weights-and-labels
02-CoreML-tools-conversion-code-with-Caffe
03-Exporting-Caffe-model-to-mlmodel-format
04-Using-the-Caffe-model-with-iOS
Advanced Vision Techniques: Face Detection
01-Introduction-to-advanced-ML-with-Vision
02-Create-the-user-interface-in-storyboard
03-Coding-the-Photo-selection
04-Coding-Face-Detection
05-Face-Detection-exercise
06-Face-Detection-solution
Advanced Face and Features Detection
01-About-Advanced-Face-Features
07-Locate-face-position-and-area-part-1-of-2
08-Locate-face-position-and-area-part-2-of-2
09-Code-to-detect-Face-features-eyes-nose-lips
10-face-features-part-2
11-face-features-part-3
12-face-features-part-4
16-Activity-Solution
15-Activity-draw-all-face-features-in-blue
Advanced - Text Detection
01-About-Text-Detection-Project
02-project-text-recog-part-1
03-project-text-recog-part-2
04-project-text-recog-part-3
05-Activity-Text-Detection
06-Solution-Text-Detection
03 Building the user interface
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