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Last Updated: Jul 29, 2026
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| Section | Weight | Objectives |
|---|---|---|
| Exploratory Data Analysis Including Data Preparation | 18% | - Visualize data - Balance and partition data - Identify methods used to clean, label, and anonymize data |
| Evaluate Business Problem Including Ethical Implications | 21% | - Understand ethical challenges in the business problem - Perform AI design thinking - Assess progress on the AI Ladder - Understand what data is available - Understand business requirements |
| Implement the Proper Model | 26% | - Implement supervised learning using classification - Implement supervised learning using regression - Implement unsupervised learning using clustering - Implement unsupervised learning using dimensional reduction |
| Monitor Models in Production | 17% | - Determine whether unfair bias exists in the model - Monitor the model in production - Assess the model |
| Refine and Deploy the Model | 18% | - Select appropriate tools - Identify operations and transformations used for feature selection and engineering - Train the model and optimize hyperparameters - Implement model explainability - Configure environment specifications for model training |
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