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Machine Learning with Core ML

  • Course Code: Artificial Intelligence - Machine Learning with Core ML
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants to Leverage the power of Apple's Core ML to create smart iOS apps

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Machine Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Leverage the power of Apple’s Core ML to create smart iOS apps 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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’ll walk through realistic and interesting examples of machine learning in the context of mobile platforms (specifically iOS). You’ll 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’ll 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 

Working in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore: 

  • Explore the concepts of machine learning and Apple’s Core ML APIs 
  • Use Core ML to understand and transform images and videos 
  • Exploit the power of using CNN and RNN in iOS applications 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Understand components of an ML project using algorithms, problems, and data 
  • Master Core ML by obtaining and importing machine learning model, and generate classes 
  • Prepare data for machine learning model and interpret results for optimized solutions 
  • Create and optimize custom layers for unsupported layers 
  • Apply CoreML to image and video data using CNN 
  • Learn the qualities of RNN to recognize sketches, and augment drawing 
  • Use Core ML transfer learning to execute style transfer on images 

Audience & Pre-Requisites 

This course is geared for attendees with Apache knowledge who wish to Leverage the power of Apple’s Core ML to create smart iOS apps. 

Pre-Requisites:  Students should have  

  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Introduction to Machine Learning 
  • Introduction to Machine Learning 
  • What is machine learning? 
  • A brief tour of ML algorithms 
  • A typical ML workflow  
  1. Introduction to Apple Core ML 
  • Introduction to Apple Core ML 
  • Difference between training and inference 
  • Inference on the edge 
  • A brief introduction to Core ML 
  • Learning algorithms  
  • Considerations  
  1. Recognizing Objects in the World 
  • Recognizing Objects in the World 
  • Understanding images 
  • Recognizing objects in the world 
  • Performing inference  
  1. Emotion Detection with CNNs 
  • Emotion Detection with CNNs 
  • Facial expressions 
  • Input data and preprocessing  
  • Bringing it all together 
  1. Locating Objects in the World 
  • Locating Objects in the World 
  • Object localization and object detection  
  • Converting Keras Tiny YOLO to Core ML 
  • Making it easier to find photos 
  • Optimizing with batches 
  1. Creating Art with Style Transfer 
  • Creating Art with Style Transfer 
  • 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’s weight 
  1. Assisted Drawing with CNNs 
  • Assisted Drawing with CNNs 
  • Towards intelligent interfaces  
  • Drawing 
  • Recognizing the user’s sketch 
  1. Assisted Drawing with RNNs 
  • Assisted Drawing with RNNs 
  • Assisted drawing  
  • Recurrent Neural Networks for drawing classification 
  • Input data and preprocessing  
  • Bringing it all together 
  1. Object Segmentation Using CNNs 
  • Object Segmentation Using CNNs 
  • Classifying pixels  
  • Data to drive the desired effect – action shots 
  • Building the photo effects application 
  • Working with probabilistic results 
  1. An Introduction to Create ML 
  • An Introduction to Create ML 
  • A typical workflow  
  • Preparing the data 
  • Creating and training a model 
  • Closing thoughts 
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