CoreML Framework: A branch of artificial intelligence based on the idea that machines should be able to learn and adapt through experience
About This Video
- 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.
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.
Table of Contents
Chapter 1 : Introduction and Designing the UI
- The Course Overview 00:03:56
- How is Machine Learning Changing the World We Live in? 00:09:42
- Designing the UI 00:09:29
- Coding Custom Classes 00:07:34
- Chapter 2 : AVFoundation, AVCaptureSession, and TapGestureRecognizer
Chapter 3 : Training and Converting a ML Model to a Core ML Model
- 3_1_T 00:09:23
- Learn How to Use Core ML Model Inside Our Project UI 00:08:34
- Converting a Trained Model to .mlmodel 00:02:20
- Chapter 4 : Importing Python ML Libraries in Spyder and Configuring Caffe
- Chapter 5 : Converting a Scikit-Learn Model to Core ML and Use it in Xcode
- Title: Machine Learning with Core ML in iOS 11
- Release date: April 2018
- Publisher(s): Packt Publishing
- ISBN: 9781788620208