Book Image

Hands-On Machine Learning with ML.NET

By : Jarred Capellman
Book Image

Hands-On Machine Learning with ML.NET

By: Jarred Capellman

Overview of this book

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.
Table of Contents (19 chapters)
1
Section 1: Fundamentals of Machine Learning and ML.NET
4
Section 2: ML.NET Models
10
Section 3: Real-World Integrations with ML.NET
14
Section 4: Extending ML.NET
Getting Started with Machine Learning and ML.NET

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.