Machine Learning with ML.NET and .NET 10: Create Practical Machine Learning Applications with C# for Prediction, Classification, and Data-Driven Solutions
Machine learning can seem intimidating when you first encounter terms such as features, labels, training pipelines, classification, regression, and model evaluation. But if you already understand the basics of C# and .NET, you do not need a data-science background, advanced mathematics, or previous machine-learning experience to begin.
Machine Learning with ML.NET and .NET 10 gives you a practical, step-by-step path from your first machine-learning concepts to building, evaluating, deploying, and maintaining useful predictive applications with C#.
Instead of overwhelming you with theory, the book develops your skills through a cumulative business-oriented project. You will work with realistic data, build ML.NET pipelines, train models, examine mistakes, compare alternatives, and gradually move toward production-ready solutions. Along the way, you will see that weak models, imperfect data, misleading metrics, and coding mistakes are normal parts of learning-and that every correctly prepared dataset, successful training run, and improved prediction is meaningful progress.
Key Features- Beginner-friendly introduction to practical machine learning for C# and .NET developers
- Step-by-step ML.NET examples using .NET 10
- Practical classification, regression, forecasting, and anomaly-detection workflows
- Clear treatment of data preparation, feature engineering, leakage prevention, and model evaluation
- Real-world guidance for ASP.NET Core integration, model deployment, monitoring, and retraining
- Review questions, practical exercises, troubleshooting guidance, and quick-reference appendices
- Production-minded techniques without unnecessary mathematical complexity
- Translate real business problems into suitable machine-learning tasks
- Prepare, clean, validate, and split data correctly
- Build maintainable ML.NET transformation and training pipelines
- Engineer numeric, categorical, and text features
- Create binary, multiclass, and regression models
- Evaluate models using accuracy, precision, recall, F1, ROC AUC, MAE, RMSE, and other useful metrics
- Use cross-validation, feature importance, AutoML, and model comparison techniques
- Work with forecasting, anomaly detection, and pretrained ONNX models
- Serve predictions through ASP.NET Core 10 applications
- Test, monitor, version, retrain, and maintain models in production
This book is ideal for C# and .NET developers, students, self-learners, and software professionals who want to enter machine learning without switching to another programming ecosystem. No previous machine-learning or data-science experience is required. Basic familiarity with C# and .NET development is recommended.
Table of ContentsChapter 1: Machine Learning Foundations with ML.NET and .NET 10
Chapter 2: Preparing Reliable Data for Machine Learning
Chapter 3: Feature Engineering and ML.NET Pipelines
Chapter 4: Predicting Customer Churn with Binary Classification
Chapter 5: Predicting Numeric Outcomes with Regression
Chapter 6: Classifying Support Requests with Multiclass Models
Chapter 7: Validating, Improving, and Explaining Models
Chapter 8: Extending ML.NET to Additional Prediction Scenarios
Chapter 9: Integrating ML.NET Models into .NET Applications
Chapter 10: Testing, Monitoring, and Managing the Model Lifecycle
Start building practical machine-learning applications with confidence. If you want to turn your existing C# and .NET skills into real predictive solutions, this book provides the structured, supportive path to help you move from your first model to production-minded machine learning.
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