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IBM C1000-206 Exam Syllabus Topics:

SectionObjectives
Foundations of Artificial Intelligence- Artificial Intelligence Concepts
  • 1. AI use cases and applications
  • 2. Types of AI
  • 3. Data and AI relationships
  • 4. History and evolution of AI
Machine Learning and Deep Learning- Machine Learning Fundamentals
  • 1. Unsupervised learning
  • 2. Reinforcement learning
  • 3. Model training and evaluation
  • 4. Supervised learning
- Deep Learning Concepts
  • 1. Deep neural networks
  • 2. Common deep learning applications
  • 3. Neural networks
Natural Language Processing and Computer Vision- Computer Vision
  • 1. Image recognition
  • 2. Object detection
  • 3. Visual AI applications
- Natural Language Processing
  • 1. Language understanding
  • 2. Text analysis
  • 3. Chatbots and conversational AI
Generative AI- Generative AI Fundamentals
  • 1. Limitations and challenges
  • 2. Large Language Models
  • 3. Content generation
  • 4. Generative AI use cases
IBM AI Technologies and Tools- IBM AI Ecosystem
  • 1. Enterprise AI solutions
  • 2. Building and running AI models
  • 3. IBM Watson Studio
  • 4. IBM watsonx platform
AI Ethics and Responsible AI- Responsible AI Principles
  • 1. Privacy and security
  • 2. Transparency and explainability
  • 3. Bias and fairness
  • 4. Ethical considerations in AI deployment

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