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| Section | Weight | Objectives |
|---|---|---|
| AI Tools, Platforms and Deployment | 20% | - AI development platforms - Model deployment and monitoring - Cloud integration for AI - IBM AI tools and services |
| AI Ethics, Governance and Use Cases | 20% | - Ethical principles in AI - AI governance and compliance - Real-world AI applications - Bias and fairness |
| Introduction to AI | 16% | - Definition and history of Artificial Intelligence - Machine learning fundamentals - Probabilistic methods in AI - Data types and structure |
| Machine Learning Algorithms and Models | 24% | - Model training and evaluation - Supervised learning - Unsupervised learning - Reinforcement learning |
| Natural Language Processing and Computer Vision | 20% | - Image and video analysis - Computer vision basics - Text processing and understanding - NLP concepts and applications |
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