Exam Code: C1000-206
Exam Name: IBM Artificial Intelligence Fundamentals v1 - Associate
Certification Provider: IBM
Corresponding Certification: IBM Certification
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IBM C1000-206 Exam Syllabus Topics:

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

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