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By opening this book, you are taking the first step in disrupting your own knowledge by approaching solutions to complex problems with machine learning. You will be achieving this with the use of Microsoft's ML.NET framework. Having spent several years applying machine learning to cybersecurity, I'm confident that the knowledge you garner from this book will not only open career opportunities to you but also open up your thought processes and change the way you approach problems. No longer will you even approach a complex problem without thinking about how machine learning could possibly solve it.

Over the course of this book, you will learn about the following:

How and when to use five different algorithms that ML.NET provides
Real-world end-to-end examples demonstrating ML.NET algorithms
Best practices when training your models, building your training sets, and feature engineering
Using pre-trained models in both TensorFlow and ONNX formats

This book does assume that you have a reasonably solid understanding of C#. If you have other experience with a strongly typed object-oriented programming language such as C++ or Java, the syntax and design patterns are similar enough to not hinder your ability to follow the book. However, if this is your first deep dive into a strongly typed language such as C#, I strongly suggest picking up Learn C# in 7 Days, by Gaurav Aroraa, published by Packt Publishing, to get a quick foundation. In addition, no prior machine learning experience is required or expected, although a cursory understanding will accelerate your learning.

In this chapter, we will cover the following:

The importance of learning about machine learning today
The model-building process
Exploring types of learning
Exploring various machine learning algorithms
Introduction to ML.NET

By the end of the chapter, you should have a fundamental understanding of what it takes to build a model from start to finish, providing the basis for the remainder of the book.

Key benefits

Get well-versed with the ML.NET framework and its components and APIs using practical examples

Learn how to build, train, and evaluate popular machine learning algorithms with ML.NET offerings

Extend your existing machine learning models by integrating with TensorFlow and other libraries

Description

Machine learning (ML) is widely used in many industries such as science, healthcare, and research and its popularity is only growing. In March 2018, Microsoft introduced ML.NET to help .NET enthusiasts in working with ML. With this book, you’ll explore how to build ML.NET applications with the various ML models available using C# code. The book starts by giving you an overview of ML and the types of ML algorithms used, along with covering what ML.NET is and why you need it to build ML apps. You’ll then explore the ML.NET framework, its components, and APIs. The book will serve as a practical guide to helping you build smart apps using the ML.NET library. You’ll gradually become well versed in how to implement ML algorithms such as regression, classification, and clustering with real-world examples and datasets. Each chapter will cover the practical implementation, showing you how to implement ML within .NET applications. You’ll also learn to integrate TensorFlow in ML.NET applications. Later you’ll discover how to store the regression model housing price prediction result to the database and display the real-time predicted results from the database on your web application using ASP.NET Core Blazor and SignalR. By the end of this book, you’ll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET.

Who is this book for?

If you are a .NET developer who wants to implement machine learning models using ML.NET, then this book is for you. This book will also be beneficial for data scientists and machine learning developers who are looking for effective tools to implement various machine learning algorithms. A basic understanding of C# or .NET is mandatory to grasp the concepts covered in this book effectively.

What you will learn

Understand the framework, components, and APIs of ML.NET using C#

Develop regression models using ML.NET for employee attrition and file classification

Evaluate classification models for sentiment prediction of restaurant reviews

Work with clustering models for file type classifications

Use anomaly detection to find anomalies in both network traffic and login history

Work with ASP.NET Core Blazor to create an ML.NET enabled web application

Integrate pre-trained TensorFlow and ONNX models in a WPF ML.NET application for image classification and object detection

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Amazon verified reviews

buyer1 Apr 24, 2020

This book offers a concise, well written and executed description of using ML.NET to approach machine learning. It uses excellent examples and provides clear discussion for the reader. A must for someone that wants a starting point to delve into machine learning using C#.The chapter on setting up the development environment is especially useful. Like the other chapters, it is well organized and written for understanding.Enjoy!

Amazon Verified review

Sankari Aug 19, 2020

Every chapter has a brief summary of the concept, followed by the corresponding hands-on using ML.NET, allowing the user to get familiarized with the framework on the fly. By the end of the book, a reader would have implemented multiple .NET core based basic Machine Learning models. The book also gives an overview on the integration of ML.NET with other frameworks such as TensorFlow, ONNX and is well explained using the existing models. It's elegant, effective and methodical without any discontinuity. It's a recommended read for an experienced .NET developer and a novice in Machine Learning.

LFP Oct 21, 2023

It's the kind of book that really teaches you what you need to know to start with ML. Goes to the point in every chapter. Of course, you need some fundamental knowledge of AI but in general, this is a good book for starters in what is machine learning.

Jeannette Dec 30, 2023

My whole experience with C# is only from creating Grasshopper plugins for Rhino3D. This book gives me the exact steps needed to setup visual studio to create ML algorithms. Pictures and examples are very concise and clear. I have great confidence I will be able to optimize my 3D CAD with machine learning with this book and a couple months of practice

Andrei Jaume Feb 09, 2021

In this volume, Capellman guides the enthusiastic software developer in the process to learn about data science concepts such as regression, classification, clustering, and anomaly detection up to a journeyman level. I would highly recommend this book to any .NET developer or intermediate data scientist who is excited about implementing different machine learning algorithms with ML.NET as this work does a fantastic job of guiding its intended target audience. In order to fully benefit from this book, a moderate knowledge of C# is required, as the book does not cover essential basic C# concepts needed to be successful in implementing the book examples, however the book does provide the reader with diverse graphs and detailed code snippets that aid the author in explaining the different machine learning ideas. Overall a great academic read.

Hands-On Machine Learning with ML.NET: Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C#

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