[100% Off] Machine Learning With C++: From Fundamentals To Deployment
From Math to Mobile AI: Building and Deploying Machine Learning with C++
What you’ll learn
- Understand the foundations of machine learning and apply mathematical concepts like linear algebra to real-world ML problems.
- Deploy machine learning models into production
- including serialization
- ONNX export
- experiment tracking
- and mobile deployment with real-time applications.
- Implement machine learning algorithms in C++
- including regression
- classification
- clustering
- anomaly detection
- and recommender systems.
- Build and optimize deep learning models such as neural networks
- convolutional networks
- and transformers
- while leveraging transfer learning techniques.
Requirements
- Basic knowledge of C++ programming (variables
- functions
- and object-oriented programming).
- Some exposure to mathematics (linear algebra
- probability
- and statistics are helpful but not mandatory).
Description
This course contains the use of artificial intelligence.This course is designed to take you on a complete journey through the world of machine learning, using one of the most powerful and performance-driven languages—C++. Unlike many machine learning resources that rely solely on Python, this course equips you with the skills to implement, optimize, and deploy models directly in C++ while still bridging the gap to popular libraries and frameworks.
We begin with the core foundations of machine learning, introducing essential concepts in mathematics, linear algebra, and regression. From there, you will learn how to work with data effectively, including parsing formats, preprocessing, and image manipulation. Once the data is prepared, we move into model evaluation and selection, exploring performance metrics, grid search techniques, and optimization strategies.Next, you will master unsupervised and supervised learning methods, including clustering, anomaly detection, classification, recommender systems, and ensemble methods. We then dive into deep learning, covering neural networks, convolutional networks, transformers, and transfer learning with BERT—all within the C++ ecosystem.
Finally, the course prepares you for the real world of ML engineering, teaching you how to serialize models, export ONNX formats, track experiments, and deploy models to mobile devices with real-time applications like object detection on Android.
By the end, you’ll have not only theoretical knowledge but also hands-on, production-ready skills to build machine learning systems from scratch in C++. Whether you’re an aspiring engineer, researcher, or developer looking to harness the power of AI at scale, this course will give you the confidence to bring your ideas to life.
