Key Features ● Step-by-step guidance to help you navigate through various machine learning tasks and techniques with ML.NET. ● Explore all aspects of ML.NET, from installation and configuration to model deployment. ● Engage in practical exercises and real-world projects to solidify your understanding.
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.
What you will learn ● Understand the basics of ML.NET and its capabilities in the machine learning landscape. ● Gain practical experience with the ML.NET Model Builder and command-line interface (CLI) to efficiently create models.
Table of Contents 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 Index
