Learn how to organize and structure your machine learning projects for real-world deployment. From directory layout to model ...

Tweaking machine learning algorithms and models won't always be for experts only, thanks to these cutting-edge projects ...

The first step to a successful ML project is to understand that these projects require different processes, terminology, workflows, and tools than those needed by traditional development.

Mention machine learning, and many common frameworks pop into mind, from “old” stalwarts like Scikit-learn to juggernauts like Google’s TensorFlow. But the field is large and diverse, and ...