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Machine Learning with Core ML in iOS 11 [Video]

More Information
  • Use Core ML framework for applications
  • Xcode 9 - what type of project to build
  • Best practices for building and using trained ML models
  • Using preexisting trained models suitable for your applications
  • Analyzing images using the Vision framework
  • Best practices for building great ML experiences
  • Developing an application with ML functionality

Core ML is an exciting new framework that makes running various machine learning and statistical models on macOS and iOS feel natively supported. The framework helps developers integrate already prepared statistical and machine learning models into their apps. You will now be able to create applications that have machine learning functionality built in.

Developers want to learn how to use the features inside Core ML to make their applications smarter when explored by users. These videos will show you just how to integrate machine learning into real-world applications. You will design the UI and create a Tap Gesture Recognizer using AVFoundations. You will be importing Python ML Libraries such as TensorFlow, Keras, Scikit-learn into the Spyder IDE, connecting Caffe dependencies, and configuring Caffe.

You will convert a Scikit-learn model—the Iris dataset—to a CoreML model in X-code to use it in your apps. You can also search for existing models and convert them into a CoreML model so that you can explore them inside X-code and add the functionality into your apps. You will have the power to build apps that display the intellectual ability to learn from the information provided by these models. Wow! This is powerful.

By the end of this course, you will be fluent in the Core ML framework upon completion. The videos will provide the tools needed to get up and running as quickly as possible.

Style and Approach

This course is a perfect mix of concepts and practice that will help you to develop a real-world, augmented-reality, iOS 11 application from scratch. With a firm grounding in the fundamentals of the Swift language, and knowledge of how to use the key frameworks, you will be able to build an interesting application.

  • Master the tools needed to get up and running with machine learning functionality in iOS 11 using the new Core ML framework.
  • Efficiently and robustly build and implement trained models in Core ML.
  • Integrate Machine Learning and Computer Vision into real-world objects, swap out trained models of all kinds, and add Core ML functionality.
Course Length 2 hours 2 minutes
Date Of Publication 23 Apr 2018
Using Coremltools for Conversion
Exploring the Converted Machine Learning Model
The Bonus Video — A Discussion on iOS Processes


Paul DeFilippi

Paul DeFilippi is an independent iOS App Developer.He has made many popular apps on iOS, such as the social media app Chuckleblok, the Phlare3 weather app, and the Pokelator calculator, in the App Store, or you can check out what he is currently working on on GitHub (github.com/PaulDeFilippi).

Core Technology Competencies:

  • iOS application development, object-oriented concepts within the MVC design pattern, UI/UX design using SketchApp
  • Swift, Objective-C, JSON, CocoaPods
  • Xcode, SketchApp, Firebase, Fabric.io, RESTful APIs

Here are a few channels through which you can reach out to him:

Website: http://pauldefilippi.com/

LinkedIn: https://www.linkedin.com/in/paul-defilippi-93622349/

GitHub: https://github.com/PaulDeFilippi?tab=repositories