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| Section | Weight | Objectives |
|---|---|---|
| Exploratory Data Analysis and Data Preparation | 25% | - Clean, label, anonymize, and transform data - Handle missing values and outliers - Balance, partition, and split datasets - Perform statistical analysis and data visualization |
| Evaluate Business Problem and Ethical Considerations | 20% | - Identify available data sources and constraints - Assess ethical, legal, and compliance implications - Apply AI design thinking and AI Ladder framework - Understand business requirements and objectives |
| Refine, Optimize and Deploy Models | 20% | - Use IBM Watson Studio and related tools - Model explainability and interpretability - Hyperparameter tuning and model optimization - Feature engineering and feature selection - Prepare environment for model deployment |
| Monitor and Maintain Models in Production | 10% | - Assess model performance and accuracy - Detect model drift and data drift - Update and retrain models as needed - Identify and mitigate bias and fairness issues |
| Select and Implement Machine Learning Models | 25% | - Supervised learning: Classification techniques - Supervised learning: Regression techniques - Unsupervised learning: Dimensionality reduction - Model selection criteria and trade-offs - Unsupervised learning: Clustering algorithms |
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