Programming ML.NET Paperback / softback

by Dino Esposito, Francesco Esposito

Part of the Developer Reference series

Paperback / softback

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Description

The expert guide to creating production machine learning solutions with ML.NET!

ML.NET brings the power of machine learning to all .NET developers— and Programming ML.NET helps you apply it in real production solutions.

Modeled on Dino Esposito’s best-selling Programming ASP.NET, this book takes the same scenario-based approach Microsoft’s team used to build ML.NET itself.

After a foundational overview of ML.NET’s libraries, the authors illuminate mini-frameworks (“ML Tasks”) for regression, classification, ranking, anomaly detection, and more.

For each ML Task, they offer insights for overcoming common real-world challenges.

Finally, going far beyond shallow learning, the authors thoroughly introduce ML.NET neural networking.

They present a complete example application demonstrating advanced Microsoft Azure cognitive services and a handmade custom Keras network— showing how to leverage popular Python tools within .NET. 14-time Microsoft MVP Dino Esposito and son Francesco Esposito show how to: Build smarter machine learning solutions that are closer to your user’s needsSee how ML.NET instantiates the classic ML pipeline, and simplifies common scenarios such as sentiment analysis, fraud detection, and price predictionImplement data processing and training, and “productionize” machine learning–based software solutionsMove from basic prediction to more complex tasks, including categorization, anomaly detection, recommendations, and image classificationPerform both binary and multiclass classificationUse clustering and unsupervised learning to organize data into homogeneous groupsSpot outliers to detect suspicious behavior, fraud, failing equipment, or other issuesMake the most of ML.NET’s powerful, flexible forecasting capabilitiesImplement the related functions of ranking, recommendation, and collaborative filteringQuickly build image classification solutions with ML.NET transfer learningMove to deep learning when standard algorithms and shallow learning aren’t enough“Buy” neural networking via the Azure Cognitive Services API, or explore building your own with Keras and TensorFlow