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IBM C1000-206 Exam Syllabus Topics:

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

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