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Hands-On Machine Learning with ML.NET

By : Capellman

4 (10)

Buy this Book

Hands-On Machine Learning with ML.NET

4 (10)

By: Capellman

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Section 1: Fundamentals of Machine Learning and ML.NET

Free Chapter

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

Setting Up the ML.NET Environment

Setting up your development environment

Creating your first ML.NET application

Evaluating the model

Summary

Section 2: ML.NET Models

Regression Model

Breaking down regression models

Creating the linear regression application

Creating the logistic regression application

Evaluating a regression model

Summary

Classification Model

Breaking down classification models

Creating a binary classification application

Creating a multi-class classification application

Evaluating a classification model

Summary

Clustering Model

Breaking down the k-means algorithm

Creating the clustering application

Evaluating a k-means model

Summary

Anomaly Detection Model

Breaking down anomaly detection

Creating a time series application

Creating an anomaly detection application

Evaluating a randomized PCA model

Summary

Matrix Factorization Model

Breaking down matrix factorizations

Creating a matrix factorization application

Evaluating a matrix factorization model

Summary

Section 3: Real-World Integrations with ML.NET

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

Using ML.NET with ASP.NET Core

Breaking down ASP.NET Core

Creating the file classification web application

Exploring additional ideas for improvements

Summary

Using ML.NET with UWP

Breaking down the UWP architecture

Creating the web browser classification application

Additional ideas for improvements

Summary

Section 4: Extending ML.NET

Training and Building Production Models

Investigating feature engineering

Obtaining training and testing datasets

Creating your model-building pipeline

Summary

Using TensorFlow with ML.NET

Breaking down Google's Inception model

Creating the WPF image classification application

Additional ideas for improvements

Summary

Using ONNX with ML.NET

Breaking down ONNX and YOLO

Creating the ONNX object detection application

Exploring additional production application enhancements

Summary

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Creating your first ML.NET application

The time has come to start creating your first ML.NET application. For this first application, we will create a .NET Core console application. This application will classify a sentence of words as either a positive statement or a negative statement, training on a small sample dataset provided. For this project, we will use a binary logistic regression classification model using the Stochastic Dual Coordinate Ascent (SDCA) method. In Chapter 3, Regression Model, we will go into greater depth on this method.

Creating the project in Visual Studio

Upon opening, and depending on your configuration in Visual Studio, it will either open directly on to the project creation screen, or will be an empty Visual Studio window. If your environment displays the latter, simply click File, then New, and then Project:

When the window opens, type console app in the search field to find Console App (.NET Core). Make sure that the language type is C# (there are Visual...