Hands-On Machine Learning with ML.NET

Machine learning can feel like a separate world from everyday software development. This book approaches it from a C# developer’s perspective, using Microsoft to build models and put them to work applications. The focus is practical: understand the machine-learning workflow, then explore algorithms through implemented examples.

Build a model step by step

The early chapters lay out the work behind a useful model: framing the problem, choosing features, gathering data, preparing a pipeline, training, and evaluating results. That foundation gives the later coding examples a clear purpose—and helps readers see where data and design decisions affect a model’s performance.

Explore algorithms in practice

Examples introduce regression, classification, clustering, anomaly detection, and matrix factorization. Along the way, the book discusses training and prediction code as well as measures used to evaluate model performance. Together, these topics show how different approaches fit different tasks, from predicting a numerical value to grouping data or generating recommendations.

Connect machine learning applications

Beyond individual models, the book explores integrating projects and using pre-trained TensorFlow and ONNX models. Its code-centered approach keeps attention on how machine learning components fit into an application, not just on the theory behind an algorithm.

A practical starting point for C# developers

A reasonably solid grasp of C# is assumed, but prior machine-learning experience is not required. Developers who want to investigate machine learning within ecosystem can use the examples to build a working foundation and become more familiar with the decisions involved in developing and evaluating models.

For readers who prefer learning by following the code from problem setup through model evaluation, this offers a structured route into.