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.

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Hands-On Machine Learning with ML.NET
Jarred Capellman
Mar, 2020 | 296 pages
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Table of Contents (19 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Section 1: Fundamentals of Machine Learning and ML.NET
Section 1: Fundamentals of Machine Learning and ML.NET
Free Chapter
2
Getting Started with Machine Learning and ML.NET
Getting Started with Machine Learning and ML.NET
The importance of learning about machine learning today
The model building process
Exploring types of learning
Exploring various machine learning algorithms
What is ML.NET?
Summary
3
Setting Up the ML.NET Environment
Setting Up the ML.NET Environment
Setting up your development environment
Creating your first ML.NET application
Evaluating the model
Summary
4
Section 2: ML.NET Models
Section 2: ML.NET Models
5
Regression Model
Regression Model
Breaking down regression models
Creating the linear regression application
Creating the logistic regression application
Evaluating a regression model
Summary
6
Classification Model
Classification Model
Breaking down classification models
Creating a binary classification application
Creating a multi-class classification application
Evaluating a classification model
Summary
7
Clustering Model
Clustering Model
Breaking down the k-means algorithm
Creating the clustering application
Evaluating a k-means model
Summary
8
Anomaly Detection Model
Anomaly Detection Model
Breaking down anomaly detection
Creating a time series application
Creating an anomaly detection application
Evaluating a randomized PCA model
Summary
9
Matrix Factorization Model
Matrix Factorization Model
Breaking down matrix factorizations
Creating a matrix factorization application
Evaluating a matrix factorization model
Summary
10
Section 3: Real-World Integrations with ML.NET
Section 3: Real-World Integrations with ML.NET
11
Using ML.NET with .NET Core and Forecasting
Using ML.NET with .NET Core and Forecasting
Breaking down the .NET Core application architecture
Creating the stock price estimator application
Exploring additional production application enhancements
Summary
12
Using ML.NET with ASP.NET Core
Using ML.NET with ASP.NET Core
Breaking down ASP.NET Core
Creating the file classification web application
Exploring additional ideas for improvements
Summary
13
Using ML.NET with UWP
Using ML.NET with UWP
Breaking down the UWP architecture
Creating the web browser classification application
Additional ideas for improvements
Summary
14
Section 4: Extending ML.NET
Section 4: Extending ML.NET
15
Training and Building Production Models
Training and Building Production Models
Investigating feature engineering
Obtaining training and testing datasets
Creating your model-building pipeline
Summary
16
Using TensorFlow with ML.NET
Using TensorFlow with ML.NET
Breaking down Google's Inception model
Creating the WPF image classification application
Additional ideas for improvements
Summary
17
Using ONNX with ML.NET
Using ONNX with ML.NET
Breaking down ONNX and YOLO
Creating the ONNX object detection application
Exploring additional production application enhancements
Summary
18
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Evaluating a classification model

As discussed in previous chapters, evaluating a model is a critical part of the overall model-building process. A poorly trained model will only provide inaccurate predictions. Fortunately, ML.NET provides many popular attributes to calculate model accuracy, based on a test set at the time of training, to give you an idea of how well your model will perform in a production environment.

In ML.NET, as noted earlier in the sample applications, there are several properties that comprise the CalibratedBinaryClassificationMetrics class object. In Chapter 2, Setting Up the ML.NET Environment, we reviewed some of these properties. However, now that we have a more complex example and have learned how to evaluate regression models, let us dive into the following properties:

  • Accuracy
  • Area Under ROC Curve
  • F1 Score
  • Area Under Precision-Recall Curve

In addition, we will also look at the following four metrics returned by the MulticlassClassificationMetrics object...

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