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IBM C1000-154 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Visualization and Storytelling | 5% | - Communicate results to stakeholders - Create effective visualizations |
| Evaluate the Model | 15% | - Validate model generalizability - Identify bias and overfitting - Assess classification/regression metrics |
| Deploy the Solution | 10% | - Deploy models as APIs in Watson - Monitor model performance post-deployment - Ensure scalability and reliability |
| Collect and Explore the Data | 15% | - Identify and access data sources in Watson Studio - Detect patterns, outliers, and correlations - Perform descriptive statistics and exploratory analysis |
| Governance and Compliance | 5% | - Data security and privacy regulations - Model governance and lineage tracking |
| Prepare the Data | 18% | - Handle missing values and outliers - Clean, transform, and normalize datasets - Use Watson tools for data preparation - Feature engineering and selection |
| Understand the Business Problem | 12% | - Apply data science methodologies (CRISP-DM) - Translate business requirements into data science objectives - Define success metrics and constraints |
| Build the Model | 20% | - Train models using Watson AutoAI and SPSS - Perform hyperparameter tuning - Compare and select best performing models - Select appropriate ML algorithms |
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