Mukherjee ML.NET Revealed

Simple Tools for Applying Machine Learning to Your Applications
1. Auflage 2020
ISBN: 978-1-4842-6543-7
Verlag: APRESS
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 174 Seiten, eBook

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 outscenarios and code samples that can be used in many real-world situations. What You Will LearnCreate a machine learning model using only the C# languageBuild confidence in your understanding of machine learning algorithms                                   Painlessly implement algorithms                                                                                 Begin using the ML.NET library softwareRecognize the many opportunities to utilize ML.NET to your advantageApply and reuse code samples from the bookUtilize the bonus algorithm selection quick references available onlineWho This Book Is For Developers who want to learn how to use and apply machine learning to enrich their applications

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Mukherjee, Sudipta

Inhaltsverzeichnis

Chapter 1: Meet ML.NET.- Chapter 2: The Pipeline.- Chapter 3: Handling Data.- Chapter 4: Regressions.- Chapter 5: Classifications.- Chapter 6: Clustering.- Chapter 7: Sentiment Analysis.- Chapter 8: Product Recommendation.- Chapter 9: Anomaly Detection.- Chapter 10: Object Detection.

Über Autor(innen)

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.