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Hands-On Machine Learning with ML.NET

You're reading from   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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Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781789801781
Length 296 pages
Edition 1st Edition
Languages
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Author (1):
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Jarred Capellman Jarred Capellman
Author Profile Icon Jarred Capellman
Jarred Capellman
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Toc

Table of Contents (19) Chapters Close

Preface 1. Section 1: Fundamentals of Machine Learning and ML.NET
2. Getting Started with Machine Learning and ML.NET FREE CHAPTER 3. Setting Up the ML.NET Environment 4. Section 2: ML.NET Models
5. Regression Model 6. Classification Model 7. Clustering Model 8. Anomaly Detection Model 9. Matrix Factorization Model 10. Section 3: Real-World Integrations with ML.NET
11. Using ML.NET with .NET Core and Forecasting 12. Using ML.NET with ASP.NET Core 13. Using ML.NET with UWP 14. Section 4: Extending ML.NET
15. Training and Building Production Models 16. Using TensorFlow with ML.NET 17. Using ONNX with ML.NET 18. Other Books You May Enjoy
Classification Model

With regression models behind us, it is now time to dive into classification models. In this chapter, we will examine the math behind classification models, as well as the various applications of classification models. In addition, we will build two new ML.NET classification applications: the first, a binary classification example that will predict if a car's price is a good deal or not, akin to what you would find on a car purchase website; the other application, a multi-class classification application that categorizes emails. Finally, we will explore how to evaluate a classification model with the properties ML.NET exposes in classification models.

In this chapter, we will cover the following topics:

  • Breaking down classification models
  • Creating a binary classification application
  • Creating a multi-class classification application
  • Evaluating a classification...
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