As we enter the last section of the book, this chapter provides an overview of using machine learning in a production environment. At this point in the book, you have learned the various algorithms that ML.NET provides, and you have created a set of three production applications. With all of this knowledge garnered, your first thought will probably be: how can I immediately create the next killer machine learning app? Prior to jumping right into answering that question, this chapter will help to prepare you for those next steps in that journey. As discussed and utilized in previous chapters, there are three major components of training a model: feature engineering, sample gathering, and creating a training pipeline. In this chapter we will focus on those three components, expanding your thought process for how to succeed in creating a production...
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Hands-On Machine Learning with ML.NET
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Hands-On Machine Learning with ML.NET
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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.
Table of Contents (19 chapters)
Preface
Section 1: Fundamentals of Machine Learning and ML.NET
Free Chapter
Getting Started with Machine Learning and ML.NET
Setting Up the ML.NET Environment
Section 2: ML.NET Models
Regression Model
Classification Model
Clustering Model
Anomaly Detection Model
Matrix Factorization Model
Section 3: Real-World Integrations with ML.NET
Using ML.NET with .NET Core and Forecasting
Using ML.NET with ASP.NET Core
Using ML.NET with UWP
Section 4: Extending ML.NET
Training and Building Production Models
Using TensorFlow with ML.NET
Using ONNX with ML.NET
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