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| Section | Weight | Objectives |
|---|---|---|
| Ethics, Governance and Future of AI | 15% | - Regulatory compliance and trust - Ethical principles and bias mitigation - Emerging trends and future directions |
| Deep Learning and Neural Networks | 20% | - Neural network fundamentals - Applications in computer vision and NLP - Deep learning architectures |
| Machine Learning Basics | 25% | - Supervised, unsupervised, reinforcement learning - Model training and evaluation - Data preparation and preprocessing |
| Introduction to Artificial Intelligence | 20% | - Types of AI: Narrow vs General - Definition and history of AI - AI applications and use cases |
| AI Tools, Platforms and IBM Solutions | 20% | - Development tools and frameworks - IBM Watson services overview - Cloud AI platforms |
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