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Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Apache knowledge and basic Linux experience seeking to create smart iOS apps with Apple's Core ML

Who should attend & recommended skills

  • This course is geared for those with Apache knowledge who wish to leverage the power of Apple’s Core ML to create smart iOS apps.
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class.
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

Core ML is a popular framework by Apple, with APIs designed to support various machine learning tasks. It allows you to train your machine learning models and then integrate them into your iOS apps. Machine Learning with Core ML is a fun and practical guide that not only demystifies Core ML but also sheds light on machine learning. In this course, you will walk through realistic and interesting examples of machine learning in the context of mobile platforms (specifically iOS). You will learn to implement Core ML for visual-based applications using the principles of transfer learning and neural networks. Having got to grips with the basics, you will discover a series of seven examples, each providing a new use-case that uncovers how machine learning can be applied along with the related concepts. By the end of the course, you will have the skills required to put machine learning to work in their own applications, using the Core ML APIs

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:
  • Using Core ML to understand and transform images and videos
  • Exploiting the power of using CNN and RNN in iOS applications
  • Components of an ML project using algorithms, problems, and data
  • Master Core ML by obtaining and importing machine learning model, and generate classes
  • Preparing data for machine learning model and interpret results for optimized solutions
  • Creating and optimize custom layers for unsupported layers
  • Applying CoreML to image and video data using CNN
  • The qualities of RNN to recognize sketches, and augment drawing
  • Using Core ML transfer learning to execute style transfer on images

Course breakdown / modules

  • What is machine learning?
  • A brief tour of ML algorithms
  • A typical ML workflow

  • Difference between training and inference
  • Inference on the edge
  • A brief introduction to Core ML
  • Learning algorithms
  • Considerations

  • Understanding images
  • Recognizing objects in the world
  • Performing inference

  • Facial expressions
  • Input data and preprocessing
  • Bringing it all together

  • Object localization and object detection
  • Converting Keras Tiny YOLO to Core ML
  • Making it easier to find photos
  • Optimizing with batches

  • Transferring style from one image to another
  • A faster way to transfer style
  • Converting a Keras model to Core ML
  • Building custom layers in Swift
  • Reducing your model weight

  • Towards intelligent interfaces
  • Drawing
  • Recognizing the user sketch

  • Assisted drawing
  • Recurrent Neural Networks for drawing classification
  • Input data and preprocessing
  • Bringing it all together

  • Classifying pixels
  • Data to drive the desired effect action shots
  • Building the photo effects application
  • Working with probabilistic results

  • A typical workflow
  • Preparing the data
  • Creating and training a model
  • Closing thoughts