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IBM C1000-206 Exam Overview:

Certification Vendor:IBM
Exam Name:IBM Artificial Intelligence Fundamentals v1 - Associate (C1000-206)
Exam Number:C1000-206
Available Languages:English
Exam Format:Multiple choice
Recommended Training:IBM AI Fundamentals Courses (Coursera)
IBM SkillsBuild - AI Learning Paths
Exam Registration:Pearson VUE IBM Exams
IBM Certification Portal
Exam Way:Online proctored exam via IBM certification platform / Pearson VUE (availability may vary by region)

IBM C1000-206 Exam Syllabus Topics:

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

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