ML.NET Revealed

Get introduced to ML.NET, a new open source, cross-platform machine learning framework from Microsoft that is intended to democratize machine learning and enable as many developers as possible.

Dive in to learn how ML.NET is designed to encapsulate complex algorithms, making it easy to consume them in many application settings without having to think about the internal details. You will learn about the features that do the necessary “plumbing” that is required in a variety of machine learning problems, freeing up your time to focus on your applications. You will understand that while the infrastructure pieces may at first appear to be disconnected and haphazard, they are not.

Developers who are curious about trying machine learning, yet are shying away from it due to its perceived complexity, will benefit from this book. This introductory guide will help you make sense of it all and inspire you to try out scenarios and code samples that can be used in many real-world situations.

WHAT YOU WILL LEARN

* Create a machine learning model using only the C# language
* Build confidence in your understanding of machine learning algorithms
* Painlessly implement algorithms
* Begin using the ML.NET library software
* Recognize the many opportunities to utilize ML.NET to your advantage
* Apply and reuse code samples from the book
* Utilize the bonus algorithm selection quick references available online

WHO THIS BOOK IS FOR

Developers who want to learn how to use and apply machine learning to enrich their applications

SUDIPTA MUKHERJEE is an electronics engineer by education and a computer scientist by profession. He holds a degree in electronics and communication engineering. He is passionate about data structure, algorithms, text processing, natural language processing tools development, programming languages, and machine learning. He is the author of several technical books. He has presented at @FuConf and other developer events, and he lives in Bangalore with his wife and son.

Chapter 01: Meet ML.NET

Chapter 02: The Pipeline

Chapter 03: Handling Data

Chapter 04: Regressions

Chapter 05: Classifications

Chapter 06: Clustering

Chapter 07: Sentiment Analysis

Chapter 08: Product Recommendation

Chapter 09: Anomaly Detection

Chapter 10: Object Detection