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IBM A1000-144 Exam Syllabus Topics:

SectionWeightObjectives
Refine, Optimize and Deploy Models20%- Hyperparameter tuning and model optimization
- Prepare environment for model deployment
- Use IBM Watson Studio and related tools
- Feature engineering and feature selection
- Model explainability and interpretability
Evaluate Business Problem and Ethical Considerations20%- Apply AI design thinking and AI Ladder framework
- Assess ethical, legal, and compliance implications
- Identify available data sources and constraints
- Understand business requirements and objectives
Monitor and Maintain Models in Production10%- Detect model drift and data drift
- Assess model performance and accuracy
- Update and retrain models as needed
- Identify and mitigate bias and fairness issues
Exploratory Data Analysis and Data Preparation25%- Handle missing values and outliers
- Clean, label, anonymize, and transform data
- Perform statistical analysis and data visualization
- Balance, partition, and split datasets
Select and Implement Machine Learning Models25%- Unsupervised learning: Clustering algorithms
- Supervised learning: Regression techniques
- Supervised learning: Classification techniques
- Model selection criteria and trade-offs
- Unsupervised learning: Dimensionality reduction

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