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 Espositos best-selling Programming ASP.NET, this book takes the same scenario-based approach Microsofts team used to build ML.NET itself. After a foundational overview of ML.NETs 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 MicrosoftAzure 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 users 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 learningbased 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.NETs 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 arent enough; Buy neural networking via the Azure Cognitive Services API, or explore building your own with Keras and TensorFlow.