I think it is not necessary to explain the meaning of such terms as machine learning and artificial intelligence in 2017. You can find a lot of op-ed articles and research papers on this topic. So, I assume that the reader is familiar with the topic and knows definitions of basic terms. When talking about machine learning, data scientists and software engineers usually mean deep neural networks that became quite popular because of their productivity. So far there are many software solutions and packages for solving artificial neural networks tasks: Caffe, TensorFlow, Torch, Theano(rip), cuDNN, etc.
Swift is an innovative protocol-oriented open source programming language written within Apple by Chris Lattner (who recently left Apple and, after SpaceX, settled down in Google).
Apple OS already features different libraries for working with matrices and vector algebra, such as BLAS, BNNS, DSP, that were later on gathered in the single Accelerate library.
In 2015, small-scale solutions based on the Metal graphics technology for implementing math appeared.
In 2016, CoreML was introduced:
CoreML can import a finished and trained model (CaffeV1, Keras, scikit-learn) and allows developer to export it to an application.
So, in the first place, you need to prepare a model on another platform using the Python or C++ language and third-party frameworks. Second, you need to educate it using a third-party hardware based solution.
Only after that you can import it and start working with the Swift language. As for me, it all seems too complicated.