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 (English Edition)
by 作者: Kalicharan Mahasivabhattu (Author), Deepti Bandi (Author)
ASIN : B0D8L3Q283
Publisher Finelybook 出版社: Orange Education Pvt. Ltd
Publication Date 出版日期: 2024-07-02
Language 语言: English
Pages 页数: 247 pages
ISBN-10 书号: 8197256373
ISBN-13 书号: 9788197256370
Book Description
“Empower Your .NET Journey with Machine Learning”
Book Description
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.
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.
Table ofContents
1. Introduction to ML.NET
2. Installing and Configuring ML.NET
3. ML.NET Model Builder and CLI
4. Collecting and Preparing Data for ML.NET
5. Machine Learning Tasks in ML.NET
6. Choosing and Tuning Machine Learning Algorithms in ML.NET
7. Inspecting and Interpreting ML.NET Models
8. Saving and Loading Models in ML.Net
9. Optimizing ML.NET Models for Accuracy
10. Deploying ML.NET Models with Azure Functions and Web API
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