Module 1: Foundations of AI (4 Hours)
- What is AI? Scope & real-world applications
- AI vs Machine Learning vs Deep Learning
- AI in Dubai’s Vision 2030 & global opportunities
- AI workflow & lifecycle
Module 2: Python for AI (6 Hours)
- Python refresher for AI (NumPy, Pandas, Matplotlib, Seaborn)
- Working with datasets (CSV, APIs, real-world data)
- Introduction to Scikit-learn & TensorFlow
Module 3: Machine Learning Essentials (12 Hours)
- Supervised learning (Regression, Classification)
- Unsupervised learning (Clustering, Dimensionality Reduction)
- Model evaluation (Accuracy, Precision, Recall, F1-score)
- Overfitting & underfitting
- Hyperparameter tuning (GridSearch, RandomizedSearch)
- Hands-on mini project: Predicting Student Performance using ML
Module 4: Deep Learning & Neural Networks (12 Hours)
- Neural networks basics (Perceptron, Activation Functions)
- Introduction to TensorFlow/Keras
- Convolutional Neural Networks (CNNs) for image processing
- Recurrent Neural Networks (RNNs, LSTMs) for sequential data
- Transfer Learning with pre-trained models (ResNet, VGG, BERT)
- Hands-on mini project: Image Classification (Cats vs Dogs / Face Mask Detection)
Module 5: AI in Real-World Applications (10 Hours)
- Natural Language Processing (NLP) basics
- Chatbots & text classification
- AI in computer vision (object detection, image recognition)
- AI in IoT & Robotics
- Case studies from healthcare, fintech, smart cities (Dubai-focused examples)
Module 6: Responsible AI (4 Hours)
- AI ethics & bias
- Fairness, accountability, transparency in AI
- Regulations & compliance (Dubai’s AI strategy, global standards)
- Future of AI: Generative AI, LLMs (ChatGPT, DALL·E, etc.)
Module 7: Capstone Project (12 Hours)
- Option A: AI-powered Student Success Predictor (using ML models)
- Option B: Smart Traffic Management using AI & computer vision (Dubai smart city use case)
- Option C: AI Chatbot for University FAQs (NLP project)
- Option D: Image recognition system for healthcare diagnostics
- Deliverables: Code implementation, Project report, Final presentation/demo
Training Timeline
| Week | Modules | Hours |
| Week 1 | Module 1 & 2: Foundations + Python for AI | 10 Hours |
| Week 2 | Module 3: Machine Learning Essentials | 12 Hours |
| Week 3 | Module 4: Deep Learning & Neural Networks | 12 Hours |
| Week 4 | Module 5, 6, 7: Applications + Ethics + Capstone Project | 26 Hours |
🏆 Learning Outcomes
✔ Understand & implement ML/DL algorithms
✔ Work with real datasets & AI frameworks (Scikit-learn, TensorFlow, Keras)
✔ Build AI-driven applications in vision, NLP, and IoT
✔ Apply ethical & responsible AI practices
✔ Deploy a complete AI project from concept to execution
Reviews
There are no reviews yet.