### Course Overview
- **Course Title:** *Cours de Certification Professionnelle en Ingénierie de l’IA* (Professional Certification Course in AI Engineering)
- **Instructor:** **School of AI** (AI Academy)
- **Target Audience:**
- Advanced learners in **AI, machine learning, or deep learning**
- Aspiring **AI engineers, ML researchers, or AI architects**
- Professionals seeking **production-level AI system development skills**
- Developers with intermediate **Python and ML knowledge** transitioning to **AI engineering roles**
- **Prerequisites:**
- Completion of an **introductory or intermediate AI/machine learning course** (or equivalent knowledge)
- Strong **Python programming** skills (functions, classes, **NumPy, Pandas**)
- Familiarity with **core ML concepts**: regression, classification, model evaluation, overfitting
- Basic **deep learning** knowledge: neural networks, model architectures
- Hands-on experience with **Jupyter Notebook, TensorFlow, or PyTorch**
- Practical **math for AI**: linear algebra, probability, calculus
- Access to a **computer (Windows/macOS/Linux)** with reliable internet for tool installation
### Curriculum Highlights
- **Key Topics Covered:**
- **Model Tuning & Optimization**: Hyperparameter tuning (Grid Search, Random Search, Bayesian Optimization), regularization, cross-validation, automated pipelines
- **Convolutional Neural Networks (CNNs)**: Image classification, object detection, convolutional/pooling/dropout layers in **TensorFlow & PyTorch**
- **Recurrent Neural Networks (RNNs)**: Time-series modeling, **LSTM/GRU** architectures, vanishing gradients, sequence-to-sequence tasks
- **Transformers & Attention Mechanisms**: Self-attention, multi-head attention, positional encoding, **BERT/GPT/T5** implementations
- **Transfer Learning & Fine-Tuning**: Pre-trained model adaptation, feature extraction, domain-specific adjustments
- **AI Agents**: Reactive/goal-oriented/multi-agent systems, real-time decision-making, simulations
- **MLOps Fundamentals**: Model deployment (**Docker, MLflow, Kubeflow**), CI/CD pipelines, versioning, scalability, monitoring
- **Key Skills Learned:**
- Build and optimize **deep learning models** for production
- Implement **CNNs, RNNs, and Transformers** from scratch
- Apply **transfer learning** to leverage pre-trained models (e.g., ResNet, BERT)
- Design **autonomous AI agents** for real-world applications
- Deploy models using **MLOps tools** (Docker, MLflow, Kubeflow)
- Debug and improve models with **advanced tuning techniques**
- Integrate **AI systems** into scalable production environments
### Course Format
- **Duration:** **16 hours** of on-demand video
- **Format:** Self-paced **online course** (lifetime access)
- **Resources:**
- **5 articles** (supplemental reading)
- **5 downloadable resources** (code templates, datasets, or guides)
- **Mobile & TV access**
- **Certificate of completion**
- Hands-on **coding exercises** (Jupyter Notebook, TensorFlow, PyTorch)
- Practical **MLOps deployment projects**
### Additional Information
- **Language:** French (course title and primary instruction)
- **Subtitles:** N/A (check Udemy for updates)
- **Last Updated:** N/A (verify on Udemy platform)
- **Student Enrollment:** **357,988+ students** across instructor’s courses
- **Instructor Rating:** **4.4/5** (based on **10,131 reviews**)
