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IBM C1000-206 Exam Syllabus Topics:

SectionObjectives
Topic 1: Machine Learning Fundamentals- Core ML concepts
  • 1. Supervised learning
    • 2. Unsupervised learning
      - Model evaluation basics
      • 1. Training vs testing
        • 2. Basic evaluation metrics
          Topic 2: AI Ethics and Responsible AI- Ethical considerations
          • 1. Transparency and explainability
            • 2. Bias in AI systems
              - Responsible AI practices
              • 1. Data privacy considerations
                • 2. Fairness and accountability
                  Topic 3: Deep Learning and Neural Networks- Neural network fundamentals
                  • 1. Perceptrons and layers
                    • 2. Activation functions overview
                      - Deep learning concepts
                      • 1. Training deep neural networks
                        • 2. Common architectures overview
                          Topic 4: 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 5: IBM AI Tools and Ecosystem- IBM Watson overview
                                  • 1. AI services and capabilities
                                    • 2. Enterprise AI applications
                                      Topic 6: Natural Language Processing and Computer Vision- NLP basics
                                      • 1. Language understanding tasks
                                        • 2. Text processing concepts
                                          - Computer vision basics
                                          • 1. Image recognition concepts
                                            • 2. Object detection overview

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