Description DeepLearning AI TensorFlow Developer Professional Certificate is a training course specializing in working with the TensorFlow framework. Today...

Practical machine learning development has advanced at a remarkable pace. This is reflected by not only a rise in actual products based on, or offering, machine learning capabilities, but also a rise in new development frameworks and methodologies, most of which are backed by open source projects. In fact, developers and researchers beginning a new project can be easily overwhelmed by the choice of frameworks offered out there. These new tools vary considerably and striking a balance between keeping up with new trends and ensuring project stability and reliability can be hard. The list below describes five of the most popular open source machine learning frameworks -- what they offer, and what use cases they can best be applied to.

Today at //Build 2018, we are excited to announce the preview of ML.NET, a cross-platform, open source machine learning framework. ML.NET will allow .NET developers to develop their own models and infuse custom ML into their applications without prior expertise in developing or tuning machine learning models. ML.NET was originally developed in Microsoft Research and evolved into a significant framework over the last decade; it is used across many product groups in Microsoft like Windows, Bing, Azure, and more . With this first preview release, ML.NET enables ML tasks like classification (e.g. Along with these ML capabilities, this first release of ML.NET also brings the first draft of .NET APIs for training models, using models for predictions, as well as the core components of this framework, such as learning algorithms, transforms, and core ML data structures.