Book Image

Machine Learning with Core ML

Book Image

Machine Learning with Core ML

Overview of this book

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 book, 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 book, you will have the skills required to put machine learning to work in their own applications, using the Core ML APIs
Table of Contents (16 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Learning algorithms 


In Chapter 1Introduction to Machine Learning, we saw many different types of learning algorithms and learned that ML is really a process of automatically discovering rules given a set of examples. The main components required for this process, specifically for supervised learning, include: 

  • Input data points: For image classification, we would require images of the domain we want to classify, for example, animals. 
  • The expected outputs for these inputs: Continuing from our previous example of image classification of animals, the expected outputs could be labels associated with each of the images, for example, cat, dog, and many more. 
  • A ML algorithm: This is the algorithm used to automatically learn how to transform the input data points into a meaningful output. These derived sets of rules are what we call the model, derived through a process of learning called training

Let's make these concepts more concrete by working through a simple example. 

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