Machine Learning and C++: A Practical Guide

The book "Machine Learning and C++" is a practical guide for developers who want to master implementing machine learning algorithms in C++. Unlike Python, where most libraries are ready to use, C++ requires a deep understanding of both the mathematical foundations and the language's specifics. This book fills that gap, offering the reader a clear path from basic concepts to creating high-performance models.

What this book is about

The book covers the full cycle of working with machine learning in C++:

• Fundamentals of linear algebra and statistics — the necessary minimum for understanding algorithms, with examples in C++ (working with matrices, vectors, calculating covariance).
• Linear regression and logistic regression — implementation from scratch using gradient descent, L1/L2 regularization.
• Support Vector Machines (SVM) — building a classifier, working with kernels, optimization via SMO.
• Decision trees and random forest — CART implementation, ensemble methods, combating overfitting.
• Neural networks — from perceptron to multi-layer networks, backpropagation, using the Eigen and Dlib libraries.
• Clustering — K-means, DBSCAN, hierarchical clustering.
• Data processing — loading CSV, normalization, one-hot encoding, handling missing values.
• Performance optimization — parallelism (OpenMP), SIMD instructions, code profiling.

Who this book is for

The book is intended for:

• C++ developers who want to integrate machine learning into their projects (robotics, games, financial systems, IoT).
• Data science professionals looking to transition from Python to C++ to increase model speed.
• Engineering students studying both machine learning and low-level programming.

What you will learn

After reading the book, you will be able to:

• Independently implement core machine learning algorithms in C++ without using third-party libraries.
• Understand how popular frameworks (TensorFlow, PyTorch) work at the C++ API level.
• Optimize code for working with large volumes of data and embed models into real-world applications.
• Integrate C++ code with Python via pybind11 for hybrid projects.

Each chapter contains ready-to-use code examples that can be compiled and run. The book is written in simple language with an emphasis on practice, not theory. All algorithms have been tested on real datasets (UCI, Kaggle).

This is not just a translation of a machine learning textbook into C++, but an independent publication that takes into account the language's features: memory management, templates, STL, multithreading. You will learn to write not only working but also efficient code.