Jarred Capellman - 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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- Book: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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Create, train, and evaluate various machine learning models such as regression, classification, and clustering using ML.NET, Entity Framework, and ASP.NET Core
Key Features- 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
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, youll 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. Youll 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. Youll 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. Youll also learn to integrate TensorFlow in ML.NET applications. Later youll 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, youll have learned how to confidently perform basic to advanced-level machine learning tasks in ML.NET.
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
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.
Table of Contents- Getting started with Machine Learning and ML.NET
- Setting up the ML.NET environment
- Regression Model
- Classification Model
- Clustering Model
- Anomaly Detection Model
- Matrix Factorization Model
- Using ML.NET with .NET Core and Forecasting
- Using ML.NET with ASP.NET
- Using ML.NET with UWP
- Training and Building Production Models
- Using Tensorflow with ML.NET
- Using ONNX with ML.NET
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Copyright 2020 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Commissioning Editor: Pravin Dhandre
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First published: March 2020
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ISBN 978-1-78980-178-1
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To my amazing wife, Amy, for completing me.
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Jarred Capellman is a Director of Engineering at SparkCognition, a cutting-edge artificial intelligence company located in Austin, Texas. At SparkCognition, he leads the engineering and data science team on the industry-leading machine learning endpoint protection product, DeepArmor, combining his passion for software engineering, cybersecurity, and data science. In his free time, he enjoys contributing to GitHub daily on his various projects and is working on his DSc in cybersecurity, focusing on applying machine learning to solving network threats. He currently lives just outside of Austin, Texas, with his wife, Amy.
AndrewGreenwald holds an MSc in computer science from Drexel University and a BSc in electrical engineering with a minor in mathematics from Villanova University. He started his career designing solid-state circuits to test electronic components. For the past 25 years, he has been developing software for IT infrastructure, financial markets, and defense applications. He is currently applying machine learning to cybersecurity, developing models to detect zero-day malware. Andrew lives in Austin, Texas, with his wife and three sons.
If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.
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 to work 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. Youll 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. Youll 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 how to integrate TensorFlow into ML.NET applications. Later, you'll discover how to store the regression model housing price prediction results in 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.
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 to data scientists and machine learning developers who are looking for effective tools to implement various machine learning algorithms. A basic understanding of C# and .NET is mandatory to grasp the concepts covered in this book effectively.
, Getting Started with Machine Learning and ML.NET, talks about what machine learning is and how important machine learning is in our society today. It also introduces ML.NET and talks in more detail about getting started with it after learning about the concepts of machine learning and how they relate.
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