While AI applications are written in Python and TypeScript, their execution rests on native code: llama.cpp and the GGUF format for local models, ONNX Runtime and TensorRT for optimised inference, OpenCV for vision, CUDA for accelerators. A C++ developer does not have to become a data scientist; you need to understand enough about models to design the layer that runs them on servers, at the edge and on devices. The examples in these courses are mostly Python; the Edge AI course also has C for microcontrollers.
Demand for efficient inference is growing in directions that native code covers: models on laptops and phones, real-time vision, industrial devices, environments with tight memory and latency limits. Quantization, runtime selection, accelerator integration and the deployment of vision models are systems problems.
The risk for a C++ developer is isolation: optimising without understanding what the applications above ask for. The courses below start from how models are trained and used and go down towards on-device inference, vision and deployment, where your skill becomes decisive.
Quantization, compact formats and inference without the cloud, for privacy and latency. The Edge AI course tells the story of llama.cpp as proof that a language model could run on a laptop CPU with careful C++ and aggressive quantization.
Detection, segmentation and classification in production, with OpenCV and optimised runtimes. Deployment and optimisation are the native-code half of a computer vision project.
Kilobytes of RAM, integer-only math and no dynamic allocation: TinyML runs quantized models with a small C++ interpreter and a static tensor arena.
With your builds and tests as verifiers. The right context, hooks and CI integration make the difference between help and noise.
Each of these changes has at least one course in the track below. See the track
When you have gone through all the lessons of a course and have an average of at least 70% on its quizzes, you issue from your account, after confirming the conditions, a Certificate of Completion with a unique number and a public verification link. It is not issued automatically and it comes at no extra cost, with a plan or with single-course access.
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In the order in which they matter. If you already have experience with model APIs, skip the foundations. For each course, the reason it matters to someone who works with C and C++.
The picture from above: what a model is, how it is trained and how it leaves Python for a native runtime.
AI and machine learning fundamentals, LLMs and modern architectures, RAG, agents and evaluation: the context you need before optimising anything.
Key modules: Machine Learning Essentials · Large Language Models and Modern Architectures · Working with LLM APIs and Integrations · AI Tools for Developers
Read the free preview Course details This course only: €99/monthTensors, autograd, backpropagation, CNNs and Transformers in PyTorch, then GPU training with CUDA and export to ONNX: where the models come from and how they are handed to the runtime you maintain.
Key modules: PyTorch Tensors and Autograd · From Linear Regression to Neural Networks · Backpropagation and Gradient Descent · Loss Functions and Optimizers
Read the free preview Course details This course only: €99/monthThe layer where native code decides: models on your own hardware, vision in production, adapted models that have to be served efficiently.
The course closest to your work: quantization, pruning and distillation, the LiteRT, ONNX Runtime, Core ML and ExecuTorch runtimes, TinyML on microcontrollers with C code, accelerators such as Jetson and Coral, and small language models with llama.cpp.
Key modules: Model Compression: Making Models Fit the Edge · Edge AI Frameworks and Runtimes · Mobile AI: iOS and Android · TinyML and Microcontrollers
Read the free preview Course details This course only: €99/monthLoRA and QLoRA, then quantization for inference in GGUF, GPTQ and AWQ and self-hosting: how adapted models are prepared for the engines that run them.
Key modules: Data: The Foundation of Fine-Tuning · Parameter-Efficient Fine-Tuning (PEFT): LoRA · QLoRA and Quantized Training · The Fine-Tuning Toolchain
Read the free preview Course details This course only: €99/monthPreprocessing with OpenCV, classification, detection and segmentation, then a lesson on deployment and edge optimisation. The code is Python with PyTorch and OpenCV; deployment is where native runtimes take over.
Key modules: Image Preprocessing and Datasets with OpenCV · Convolutional Neural Networks for Classification · Object Detection · Segmentation and Segment Anything
Read the free preview Course details This course only: €99/monthThe stage where you tie your components to the rest of the system: the pipeline that delivers models, the tools exposed to agents, the way code is written.
Model registry and versioning, serving, deployment strategies and drift monitoring: the pipeline your runtime receives models from and reports metrics to.
Key modules: Experiment Tracking and Reproducibility · Data and Feature Management · Training Pipelines and Orchestration · Model Registry, Versioning, and CI/CD for ML
Read the free preview Course details This course only: €99/monthProtocol architecture, stdio and HTTP transports, authentication and security, so that you can expose systems and hardware to agents safely. The examples are in Python and TypeScript; the protocol is language-agnostic.
Key modules: Building MCP Servers with Python · Building MCP Servers with TypeScript · Transport, Authentication and Configuration · Enterprise Patterns and Production Architectures
Read the free preview Course details This course only: €99/monthAgentic coding on large codebases, with deterministic hooks and CI: your builds and tests become the verifiers of generated code.
Key modules: Multi-File and Large Codebases: Plan, Edit, Review and Context Management · The Full Git Workflow: Branches, Commits, Conflicts, Code Review and PRs · Headless Mode (claude -p) and Non-Interactive Automation · Subagents and Agent Teams: Orchestration, Delegation and Controlled Parallelization
Read the free preview Course details This course only: €99/monthThe course in the track that ends with a capstone project. The list comes from the real modules of the courses, not from a brochure.
What already exists, so that you do not start from zero. The courses in the track teach you what to build with it.
These are the skills you practise in the courses of the track; results depend on the time you invest and on your previous experience.
Start the C and C++ trackCode examples, architectures and case studies, and in some courses a capstone project at the end. Not summaries of documentation, but structured material that you go through in order, at your own pace.
You ask in the context of the lesson, by typing or by voice, and get an explanation of the concept you are on. Every lesson has its quiz: about 355 questions per course on average. The professor is an AI system, not a human teacher, included in every plan with a fair-use limit.
Agentic coding, MCP, RAG, agents, evals, LLM security, fine-tuning, AIOps, computer vision and Edge AI are whole courses in the catalog, not closing chapters.
A preview of the first lesson of every course can be read without an account. Then you choose a subscription by number of courses, from €99 a month for one course to €319 a month for 10 courses, VAT included; you pick the courses and can change them at every renewal. You can stop the renewal anytime from your account; access stays until the end of the period already paid for.
You pick them, with the AI Professor included, and can change them at every renewal. The whole track fits in a subscription of 10 courses (€319 a month, VAT included); with the full catalog (€499) you have every course.
Pay for the tier, then pick the courses in your account. You can change them at every renewal.
€99 a course
€99a month €990a year, 12 months of access
under €67 a course
€199a month €1,990a year, 12 months of access
under €52 a course
€259a month €2,590a year, 12 months of access
under €32 a course
€319a month €3,190a year, 12 months of access
under €24 a course
€359a month €3,590a year, 12 months of access
under €16 a course
€399a month €3,990a year, 12 months of access
under €10 a course, across all 50
€499a month €4,990a year, 12 months of access
Total prices, VAT included. Move up anytime and pay only the difference for the days left; move down at renewal. Renews automatically, you stop it anytime from your account; your progress and certificates stay. Digital content with immediate access: see the withdrawal terms.
A subscription by number of courses: 1, 3, 5, 10, 15 or 25 courses a month, or the full catalog. You pick them and can change them at every renewal. Compare all the subscriptions · The team library, priced per member · Give a month of access as a gift
The same skills, seen from each ecosystem. The languages close to C and C++ are highlighted.
The Edge AI course has C code for microcontrollers, next to Python, Kotlin and Swift for its other targets. Everything else on this track is in Python, the language in which training and evaluation are orchestrated. The courses are chosen for the areas where native code decides performance: on-device inference, vision and deployment.
The Edge AI course is the entry point: model compression, runtimes, TinyML on microcontrollers, accelerators and small language models on device. The computer vision course adds deployment and edge optimisation, and the fine-tuning course covers quantization for inference.
To read it, yes: training pipelines, model export and evaluation tools are in Python, and the courses use it in their examples. The execution side, runtimes, kernels and hardware integration, stays native.
All the courses recommended here are IT courses. You pick them in a subscription by number of courses: usually 3, 5 or 10 courses a month for one language and stack; there are also 15 and 25 courses, a single course and the full catalog. You pick the courses and can change them at every renewal. The subscription renews automatically, and you can stop the renewal anytime from your account. Every price, VAT included, is shown below and on the pricing page. Every course has a free preview of its first lesson, readable without an account.
8 courses, ~200 hours, all in a subscription of 10 courses: €319 per month, VAT included. You can change them at every renewal; the AI Professor is built into every lesson.
The courses are for educational purposes only: they are not professional advice, and completing them is not a diploma or a state-recognised qualification. The names of languages, tools and products belong to their owners and are mentioned for identification only. Details in the Terms & Conditions.
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