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IBM C1000-206 Exam Syllabus Topics:

SectionObjectives
Topic 1: 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
          Topic 2: AI Ethics and Responsible AI- Responsible AI practices
          • 1. Fairness and accountability
            • 2. Data privacy considerations
              - Ethical considerations
              • 1. Bias in AI systems
                • 2. Transparency and explainability
                  Topic 3: Machine Learning Fundamentals- Model evaluation basics
                  • 1. Basic evaluation metrics
                    • 2. Training vs testing
                      - Core ML concepts
                      • 1. Supervised learning
                        • 2. Unsupervised learning
                          Topic 4: Natural Language Processing and Computer Vision- Computer vision basics
                          • 1. Image recognition concepts
                            • 2. Object detection overview
                              - NLP basics
                              • 1. Language understanding tasks
                                • 2. Text processing concepts
                                  Topic 5: Introduction to Artificial Intelligence- AI applications
                                  • 1. Real-world use cases
                                    • 2. Industry adoption scenarios
                                      - AI concepts and definitions
                                      • 1. Types of AI systems
                                        • 2. What is Artificial Intelligence
                                          Topic 6: IBM AI Tools and Ecosystem- IBM Watson overview
                                          • 1. AI services and capabilities
                                            • 2. Enterprise AI applications

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