Introduction to Core ML and Machine Learning in iOS

July 3rd, 2017  | By

One thing has been cleared in WWDC 2017 is the vision of Machine learning on the device.  Core ML is a brand new machine learning framework, announced during this year’s WWDC, that comes along with iOS 11. It is used to integrate machine learning models into your iOS app programming. The best part about Core ML is that you don’t require extensive knowledge about neural networks or machine learning. Another bonus feature about Core ML is that you can use pre-trained data models as long as you convert it into a Core ML model.

IOS Machine Learning

Machine learning is a type of artificial intelligence where computers “learn” without being explicitly programmed. Instead of coding an algorithm, machine learning tools enable computers to develop and refine algorithms, by finding patterns in huge amounts of data.

What’s Core ML

Core ML integrate a broad variety of machine learning model types into your app. In addition to supporting extensive deep learning with over 30 layer types, it also supports standard models such as tree ensembles, SVMs, and generalized linear models. Because it’s built on top of low-level technologies like Metal and Accelerate, Core ML seamlessly takes advantage of the CPU and GPU to provide maximum performance and efficiency. You can run machine learning models on the device so data doesn't need to leave the device to be analyzed. Such featured native mobile application development tools make application development with ease.


By using Core ML Framework we can add artificial intelligence in our application. Apple has made it so simple to integrate different machine learning models into our apps. This opens up many possibilities for developers to build features such as image recognition, natural language processing (NLP), text prediction, etc

You can easily build computer vision machine learning features into your app. Supported features include face tracking, face detection, landmarks, text detection, rectangle detection, barcode detection, object tracking etc.

Natural Language Processing

The natural language processing APIs in Foundation use machine learning to deeply understand text using features such as language identification, tokenization, lemmatization, part of speech, and named entity recognition.

Tokenization is the process of replacing sensitive data with unique identification symbols that retain all the essential information about the data without compromising its security.

Lemmatization is the process of grouping together the inflected forms of a word so they can be analyzed as a single item.


I hope now you have understood what is Core ML framework and how you can get the benefit of Artificial Intelligence by adding this Core ML framework in iOS application development. We'll be exploring more details about CoreML in upcoming articles.

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