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 needs
See how ML.NET instantiates the classic ML pipeline, and simplifies common scenarios such as sentiment analysis, fraud detection, and price prediction
Implement data processing and training, and "productionize" machine learning-based software solutions
Move from basic prediction to more complex tasks, including categorization, anomaly detection, recommendations, and image classification
Perform both binary and multiclass classification
Use clustering and unsupervised learning to organize data into homogeneous groups
Spot outliers to detect suspicious behavior, fraud, failing equipment, or other issues
Make the most of ML.NET's powerful, flexible forecasting capabilities
Implement the related functions of ranking, recommendation, and collaborative filtering
Quickly build image classification solutions with ML.NET transfer learning
Move 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
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 needs
See how ML.NET instantiates the classic ML pipeline, and simplifies common scenarios such as sentiment analysis, fraud detection, and price prediction
Implement data processing and training, and "productionize" machine learning-based software solutions
Move from basic prediction to more complex tasks, including categorization, anomaly detection, recommendations, and image classification
Perform both binary and multiclass classification
Use clustering and unsupervised learning to organize data into homogeneous groups
Spot outliers to detect suspicious behavior, fraud, failing equipment, or other issues
Make the most of ML.NET's powerful, flexible forecasting capabilities
Implement the related functions of ranking, recommendation, and collaborative filtering
Quickly build image classification solutions with ML.NET transfer learning
Move 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
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