Hands-On Machine Learning with C++

Machine learning becomes more concrete when you can follow the data, the mathematics, and the implementation in one place. In Hands-On Machine Learning with C++, Kirill Kolodiazhnyi introduces core machine learning ideas through C++ examples, moving from foundational concepts toward practical work with data and models.

The early chapters establish a useful base in supervised and unsupervised learning, linear algebra, tensors, and linear regression. From there, the book examines how to prepare data, assess model performance, and work with algorithms using C++ libraries. Readers who already know C++ and want to apply it to machine learning will find a code-centered route through the subject.

Start with the building blocks

The book connects machine learning fundamentals with the mathematical tools behind them. Linear algebra operations, tensor representation, and linear regression are introduced alongside examples using libraries such as Eigen, Shark-ML, Dlib, and Shogun. This pairing helps readers see how abstract concepts are represented and used in working C++ code.

Make data workable

Practical modeling depends on preparing inputs well. Chapters explore reading formats including CSV, JSON, and HDF5, organizing information into C++ structures, working with images, and normalizing data. These details give the algorithms a real computational setting rather than leaving data preparation as an afterthought.

Evaluate models with care

Model performance is more than a single score. The book introduces regression and classification measures, including error metrics, precision and recall, F-scores, and AUC–ROC. It also addresses bias and variance, regularization, cross-validation, and grid search—ideas that help readers compare models and make more informed selection decisions.

Explore machine learning algorithms in C++

Algorithm chapters include clustering approaches such as k-means, hierarchical, spectral, and graph-based methods; anomaly-detection techniques including isolation forests and one-class SVMs; and dimensionality-reduction methods such as PCA, LDA, and kernel PCA. Examples across several C++ libraries show different ways these methods can be put into practice.

A practical fit for C++ learners

This book is suited to C++ programmers interested in machine learning, as well as data analysts, data scientists, and developers looking for an implementation-focused introduction. Its progression from fundamentals and data handling to evaluation and algorithms makes it useful for readers who want to understand both what a method does and how it can be expressed in C++.