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IBM C1000-206 Exam Syllabus Topics:

SectionObjectives
Deep Learning and Neural Networks- Deep learning concepts
  • 1. Training deep neural networks
    • 2. Common architectures overview
      - Neural network fundamentals
      • 1. Perceptrons and layers
        • 2. Activation functions overview
          IBM AI Tools and Ecosystem- IBM Watson overview
          • 1. Enterprise AI applications
            • 2. AI services and capabilities
              Machine Learning Fundamentals- Model evaluation basics
              • 1. Training vs testing
                • 2. Basic evaluation metrics
                  - Core ML concepts
                  • 1. Supervised learning
                    • 2. Unsupervised learning
                      Natural Language Processing and Computer Vision- NLP basics
                      • 1. Language understanding tasks
                        • 2. Text processing concepts
                          - Computer vision basics
                          • 1. Object detection overview
                            • 2. Image recognition concepts
                              AI Ethics and Responsible AI- Ethical considerations
                              • 1. Bias in AI systems
                                • 2. Transparency and explainability
                                  - Responsible AI practices
                                  • 1. Data privacy considerations
                                    • 2. Fairness and accountability
                                      Introduction to Artificial Intelligence- AI applications
                                      • 1. Real-world use cases
                                        • 2. Industry adoption scenarios
                                          - AI concepts and definitions
                                          • 1. What is Artificial Intelligence
                                            • 2. Types of AI systems

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