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IBM A1000-144 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitor Models in Production | 17% | - Assess the model - Monitor the model in production - Determine whether unfair bias exists in the model |
| Topic 2: Refine and Deploy the Model | 18% | - Train the model and optimize hyperparameters - Identify operations and transformations used for feature selection and engineering - Implement model explainability - Select appropriate tools - Configure environment specifications for model training |
| Topic 3: Exploratory Data Analysis Including Data Preparation | 18% | - Identify methods used to clean, label, and anonymize data - Visualize data - Balance and partition data |
| Topic 4: Evaluate Business Problem Including Ethical Implications | 21% | - Understand what data is available - Understand business requirements - Assess progress on the AI Ladder - Understand ethical challenges in the business problem - Perform AI design thinking |
| Topic 5: Implement the Proper Model | 26% | - Implement unsupervised learning using clustering - Implement supervised learning using regression - Implement supervised learning using classification - Implement unsupervised learning using dimensional reduction |
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