β Scribed by Sudipta Mukherjee
No coin nor oath required. For personal study only.
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 βplumbingβ 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
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
Computer Science; Microsoft and .NET
π SIMILAR VOLUMES
Beginning user level
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