Complete iOS 11 Machine Learning Masterclass

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86 Lessons (6.5h)

  • 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
    Congratulations0:48
  • 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-Vision1:25
    02-Create-the-user-interface-in-storyboard
    03-Coding-the-Photo-selection
    04-Coding-Face-Detection
    05-Face-Detection-exercise
    06-Face-Detection-solution3:47
  • Advanced Face and Features Detection
    01-About-Advanced-Face-Features1:44
    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-blue1:16
  • Advanced - Text Detection
    01-About-Text-Detection-Project1:40
    02-project-text-recog-part-13:33
    03-project-text-recog-part-2
    04-project-text-recog-part-3
    05-Activity-Text-Detection0:45
    06-Solution-Text-Detection2:09

Become A Master of Smart App Development

iO
iOS Online Courses

Yohann Taleb is a leading expert in mobile game programming, app flipping and reskinning. Besides owning over 100 apps in the Apple and Android app, Yohann has helped dozens of students publish apps that have topped the iTunes Store leaderboards and been covered by publications like WIRED.

Description

Ready to start building professional, career-boosting mobile apps? We sure hope so. Machine learning is one of the fastest growing fields in tech and many apps are starting to integrate machine learning to add a layer of intelligence. Only the smart will survive. In this comprehensive course, you'll learn how to build apps for the brand new iOS 11 while incorporating machine learning, turning them into smart apps that will interact and learn from users to enhance the user experience.

  • Access 86 lectures & 6.5 hours of content 24/7
  • Explore the three fundamental branches of applied machine learning: image & video processing, text analysis, & speech & language recognition
  • Learn how to apply machine learning to real projects
  • Perform image recognition in an app by differentiating between people & objects, & classifying flowers
  • Add Siri Voice into your app
  • Find trained machine learning models & make them ready to use in iOS apps
  • Receive in-class support

Specs

Details & Requirements

  • Length of time users can access this course: lifetime access
  • Access options: web streaming, mobile streaming
  • Certification of completion not included
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels

Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.