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) - Couverture souple

Livre 1 sur 2: Machine Learning Engineer (ML) ? Advanced Path

Mahasivabhattu, Kalicharan ; Bandi, Deepti

9788197256370: 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)

Couverture souple

ISBN 10 : 8197256373 ISBN 13 : 9788197256370

Editeur : Orange Education Pvt. Ltd, 2024

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“Empower Your .NET Journey with Machine Learning”

Take your skills to the next level with ML.NET for machine learning and generative AI. This practical guide delivers step-by-step coverage for data preparation and preprocessing for ML.NET workflows, regression, classification, and clustering with ML.NET, and automated deployment of ML.NET solutions with Web API in a way that’s natural, accessible, and designed for real-world projects.

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’ll Learn Inside:

Hands-on instructions for step-by-step ML.NET model builder and CLI guide to help you quickly build accurate models
Proven approaches for deploying ML.NET models with Azure Functions and automating model deployment for enterprise applications
Guidance on model persistence and sharing across ML.NET projects—collaborate and optimize performance
Clear techniques for ML.NET accuracy optimization, interpretability, and algorithm tuning in production settings
Practical advice for integrating data science ML.NET projects into .NET workflows
Coverage of machine learning book for .NET developers including robust model evaluation strategies
Actionable steps for model interpretability in ML.NET projects and building reliable pipelines from start to finish

Who Should Read This Book?

Developers and data scientists looking for a modern ML.NET book to master machine learning .NET solutions
Teams needing straightforward guides for Azure Functions ML.NET, .NET machine learning, and web API deployment
Professionals focused on model persistence, algorithm tuning, and performance optimization.

Why Choose This Guide?

Policy-compliant, original content, crafted with Amazon-approved keywords and aligned tightly with your actual book focus
Natural, easy-to-read tone for maximum user benefit and SEO performance—no unauthorized brands or character names.

Start optimizing .NET machine learning solutions with ML.NET—discover actionable techniques and expert strategies today!

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

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