The post starts with the basics. Building a linear model in Python with scikit-learn. Converting it to an .mlmodel. Hooking it up to UIKit controls.
“Model building is difficult, and this isn’t the right post for a deep dive into model selection and performance.”
The model uses a couple features from a Boston data set to predict house prices. A simple problem space to wrap your head around.
“Core ML makes working with machine learning and statistical models more convenient and idiomatic.
Nonetheless, it is worth ending with a caveat. Statistics and machine learning are not simply APIs. They are entire fields of study that are equal parts art and science. Developing and selecting an informative model requires practice and study.”
I completely agree. The same can be said about good graphic design. Having an expert create it and then going through the process of integrating the design into your app are two different things.
What makes Core ML interesting is how little it asks of the developer who already has their .mlmodel in hand. It’s an approach to machine learning that says, “We’ll bring this technology to you instead of making you come to us.”