1. Supervised Learning (Regression, Classification) (4 hours)
2. Unsupervised Learning (Clustering, Dimensionality Reduction) (3 hours)
3. Model Evaluation & Validation (3 hours)
4. Feature Engineering (4 hours)
5. Scikit-Learn Mastery (3 hours)
6. Project: End-to-End ML Pipeline (6 hours)

1. Neural Networks Fundamentals (3 hours)
2. TensorFlow & Keras (3 hours)
3. CNNs for Computer Vision (4 hours)
4. RNNs & LSTMs for Sequence Data (3 hours)
5. Transfer Learning (4 hours)
6. Project: Image Classifier (6 hours)

1. ML Model Deployment (3 hours)
2. Containerization with Docker (3 hours)
3. REST APIs for ML Models (3 hours)
4. MLOps Fundamentals (3 hours)
5. Project: Deploy ML Model as API (6 hours)

1. Natural Language Processing (NLP) (4 hours)
2. Generative AI & Transformers (3 hours)
3. Reinforcement Learning Basics (3 hours)
4. Edge AI & Mobile Deployment (3 hours)
5. Project: Chatbot Development (7 hours)

1. AI-Powered Code Generation (3 hours)
2. Automated Testing with AI (3 hours)
3. AI for Code Optimization (3 hours)
4. AI-Assisted Debugging (3 hours)
5. Project: AI-Augmented Development (8 hours)

Students will develop an end-to-end AI solution addressing real-world problems, incorporating all learned concepts.

Project Ideas:

• Intelligent Document Processing System
• Predictive Maintenance Application
• AI-Powered Recommendation Engine
• Automated Code Review System
• Smart Chatbot with NLP

Final Presentation & Demo: (4 hours)