Hands-On Machine Learning with ML.NET
by: Jarred Capellman
Print Length 页数: 256 pages
Edition 版次: 1
Language 语言: English
Publisher finelybook 出版社: Packt Publishing
Publication Date 出版日期: 2020-04-09
ISBN-10: 1789801788
ISBN-13: 9781789801781
Book Description
By finelybook
Book Description
By finelybook
Create,train,and evaluate various machine learning models such as regression,classification,and clustering using ML.NET,Entity Framework,and ASP.NET Core
Key Features
Get well-versed with the ML.NET framework and its components and APIs using practical examples
Learn how to build,train,and evaluate popular machine learning algorithms with ML.NET offerings
Extend your existing machine learning models by integrating with TensorFlow and other libraries
Book Description
By finelybook
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.
What you will learn
Understand the ML.NET framework,components,and APIs using C# code
Develop regression models using ML.NET for housing price predictions
Train and evaluate classification models for sentiment prediction of your website comments
Work with clustering models for customer segmentation of your product for sales
Display the real-time-saved predicted results in your web application using ASP.NET Core Blazor and SignalR
Work with Entity Framework (EF) and Web APIs in ASP.NET Core Blazor
Discover how to integrate TensorFlow in an ML.NET application
Who This Book Is For
If you are a .NET developer who wants to implement machine learning models using ML.NET,then this book is for you. This book will also be beneficial for data scientists and machine learning developers who are looking for effective tools to implement various machine learning algorithms. A basic understanding of C# or .NET is mandatory to grasp the concepts covered in this book effectively.
Contents
Preface
Section 1: Fundamentals of Machine Learning and ML.NET
Chapter 1: Getting Started with Machine Learning and ML.NET
Chapter 2: Setting Up the ML.NET Environment
Section 2: ML.NET Models
Chapter 3: Regression Model
Chapter 4: Classification Model
Chapter 5: Clustering Model
Chapter 6: Anomaly Detection Model
Chapter 7: Matrix Factorization Model
Section 3: Real-World Integrations with ML.NET
Chapter 8: Using ML.NET with.NET Core and Forecasting
Chapter 9: Using ML.NET with ASP.NET Core
Chapter 10: Using ML.NET with UWP
Section 4: Extending ML.NET
Chapter 11: Training and Building Production Models
Chapter 12: Using TensorFlow with ML.NET
Chapter 13: Using ONNX with ML.NET
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Index
