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IBM C1000-144 Exam Syllabus Topics:

SectionWeightObjectives
Model Refinement and Deployment18%- Model lifecycle operations
  • 1. Hyperparameter tuning
    • 2. Tool selection
      • 3. Training environment configuration
        • 4. Feature engineering and selection
          • 5. Model explainability
            Model Monitoring in Production17%- Operational monitoring
            • 1. Model performance evaluation
              • 2. Production monitoring
                • 3. Bias detection and fairness assessment
                  Model Implementation26%- Machine learning methods
                  • 1. Dimensionality reduction techniques
                    • 2. Supervised learning: regression
                      • 3. Supervised learning: classification
                        • 4. Unsupervised learning: clustering
                          Exploratory Data Analysis and Data Preparation18%- Data preparation techniques
                          • 1. Data cleaning, labeling, anonymization
                            • 2. Data visualization
                              • 3. Data balancing and partitioning
                                Evaluate business problem including ethical implications21%- Business requirements understanding
                                • 1. Ethical challenges in AI/ML problem framing
                                  • 2. Understand available data
                                    • 3. AI Ladder assessment
                                      • 4. AI design thinking process
                                        • 5. Understand business requirements

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