ML.NET is an open-source cross-platform machine learning framework for NET developers, with the help of this framework you can create a custom ML model using C # or F #, without leaving the .NET ecosystem. ML.NET becomes another machine learning framework in online or offline scenarios.

Use the same framework behind recognized Microsoft features like Windows Hello, Bing Ads, and PowerPoint Design Ideas to power your own applications.

There are a few key points and reasons here. The first is that the book starts all the way from installing the tools and performing all of the basic coding that will be needed later in the book. This fundamentals in the first portion is something that many other books I have purchased did not cover. The book actually exposed me to a few new libraries that I found personally useful including a hands on approach with numl and accord.net,

With the meteoric rise of Machine learning, developers are now keen on finding out how can they make their .Net applications smarter. Also, .NET developers are interested into moving into the world of devices and how to apply machine learning techniques to, well, machines. This book is packed with real-world examples to easily use machine learning techniques in your business applications. You will begin with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model using an example which predicts sales of a product.

Alena Dzenisenka is a young researcher in the field of theoretical mathematical abstractions and innovative algorithmic models possible in modern programming concepts. She is a member of F# Software Foundation Board of Trustees. Alena currently works as a Software Architect and has more than 10 years of professional experience including complex distributed systems and cloud computing. Software is Changing the World. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community.