ML.NET Revealed : Simple Tools for Applying Machine Learning to Your Applications

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 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 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 Front Matter ....Pages i-xviii Meet ML.NET (Sudipta Mukherjee)....Pages 1-21 The Pipeline (Sudipta Mukherjee)....Pages 23-36 Handling Data (Sudipta Mukherjee)....Pages 37-51 Regressions (Sudipta Mukherjee)....Pages 53-72 Classifications (Sudipta Mukherjee)....Pages 73-91 Clustering (Sudipta Mukherjee)....Pages 93-112 Sentiment Analysis (Sudipta Mukherjee)....Pages 113-127 Product Recommendation (Sudipta Mukherjee)....Pages 129-144 Anomaly Detection (Sudipta Mukherjee)....Pages 145-158 Object Detection (Sudipta Mukherjee)....Pages 159-170 Back Matter ....Pages 171-174