Название: Ultimate Machine Learning with ML.NET: Build, Optimize, and Deploy Powerful Machine Learning Models for Data-Driven Insights with ML.NET, Azure Functions, and Web API
Автор: Kalicharan Mahasivabhattu, Deepti Bandi
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 247
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

“Empower Your .NET Journey with Machine Learning”.

Dive into the world of Machine Learning for data-driven insights and seamless integration in .NET applications with the Ultimate Machine Learning with ML.NET.

The book begins with foundations of ML.NET and seamlessly transitions into practical guidance on installing and configuring it using essential tools like Model Builder and the command-line interface. Next, it dives into the heart of Machine Learning tasks using ML.NET, exploring classification, regression, and clustering with its versatile functionalities.

It will delve deep into the process of selecting and fine-tuning algorithms to achieve optimal performance and accuracy. You will gain valuable insights into inspecting and interpreting ML.NET models, ensuring they meet your expectations and deliver reliable results. It will teach you efficient methods for saving, loading, and sharing your models across projects, facilitating seamless collaboration and reuse.

ML.NET is particularly distinctive for its deep integration with the .NET ecosystem, enabling developers to leverage their existing expertise in C# and other .NET languages to build and deploy Machine Learning models seamlessly. Whether you are a seasoned software engineer or a budding data scientist, ML.NET opens the door to a world of opportunities in Machine Learning, providing a platform where creativity and problem-solving converge.

ML.NET is known for its versatility, offering support for a wide range of Machine Learning tasks, including classification, regression, clustering, and recommendation systems. Its open-source nature, platform independence, and comprehensive documentation make it a preferred choice for many practitioners who seek a framework that is both powerful and easy to learn. Whether you are working on web applications, mobile apps, or desktop software, ML.NET is designed to be a part of your toolkit, making Machine Learning accessible and attainable for everyone.

The final section of the book covers advanced techniques for optimizing model accuracy and refining performance. You will be able to deploy your ML.NET models using Azure Functions and Web API, empowering you to integrate Machine Learning solutions seamlessly into real-world applications.

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