Machine Learning with Core ML in iOS 11

Course details

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 modelthe Iris datasetto 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

About the Author

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.

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, RESTful APIs