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Hands-On Machine Learning with ML.NET

By : Capellman

4 (10)

Hands-On Machine Learning with ML.NET

4 (10)

By: 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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Section 1: Fundamentals of Machine Learning and ML.NET

Section 1: Fundamentals of Machine Learning and ML.NET

Free Chapter

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

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

Section 2: ML.NET Models

Section 2: ML.NET Models

Regression Model

Regression Model

Breaking down regression models

Creating the linear regression application

Creating the logistic regression application

Evaluating a regression model

Summary

Classification Model

Classification Model

Breaking down classification models

Creating a binary classification application

Creating a multi-class classification application

Evaluating a classification model

Summary

Clustering Model

Clustering Model

Breaking down the k-means algorithm

Creating the clustering application

Evaluating a k-means model

Summary

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

Matrix Factorization Model

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

Section 3: Real-World Integrations with ML.NET

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

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

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

Section 4: Extending ML.NET

Section 4: Extending ML.NET

Training and Building Production Models

Training and Building Production Models

Investigating feature engineering

Obtaining training and testing datasets

Creating your model-building pipeline

Summary

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

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

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Summary

Over the course of this chapter, we have deep-dived into what goes into production-ready model training from the original purpose question to a trained model. Through this deep dive, we have examined the level of effort that is needed to create detailed features through production thought processes and feature engineering. We then reviewed the challenges, the ways to address the training, and how to test dataset questions. Lastly, we also dove into the importance of an actual model building pipeline, using an entirely automated process.

In the next chapter, we will utilize a pre-built TensorFlow model in a WPF application to determine if a submitted image contains certain objects or not. This deep dive will explore how ML.NET provides an easy-to-use interface for TensorFlow models.

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