C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This course makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples. This course will get you with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You will cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you will explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you will learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format. By the end of this C++ course, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.