When it comes to combining C++ and machine learning, there are several libraries and frameworks that you can use to implement machine learning algorithms and models within a C++ environment. Some popular options include:

1. TensorFlow C++ API: TensorFlow is a widely used machine learning framework, and it provides a C++ API that allows you to build and run machine learning models using C++. This is particularly useful if you’re already familiar with TensorFlow and want to integrate machine learning into your C++ applications.
2. OpenCV: OpenCV is primarily known for computer vision, but it also includes machine learning functionalities. It’s a C++ library that provides various tools for image and video analysis, including machine learning algorithms.
3. MLPACK: MLPACK is a C++ machine learning library that offers a variety of algorithms for classification, regression, clustering, and more. It’s designed to be efficient and easy to use.
4. Shogun: Shogun is another C++ library that focuses on machine learning. It provides a wide range of algorithms for classification, regression, clustering, and other tasks.
5. dlib: dlib is a general-purpose C++ library that includes machine learning and computer vision tools. It’s known for its facial recognition capabilities and offers various machine learning algorithms.