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IBM A1000-144 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Exploratory Data Analysis and Data Preparation | 25% | - Balance, partition, and split datasets - Perform statistical analysis and data visualization - Clean, label, anonymize, and transform data - Handle missing values and outliers |
| Monitor and Maintain Models in Production | 10% | - Detect model drift and data drift - Update and retrain models as needed - Identify and mitigate bias and fairness issues - Assess model performance and accuracy |
| Refine, Optimize and Deploy Models | 20% | - Model explainability and interpretability - Feature engineering and feature selection - Use IBM Watson Studio and related tools - Prepare environment for model deployment - Hyperparameter tuning and model optimization |
| Select and Implement Machine Learning Models | 25% | - Unsupervised learning: Dimensionality reduction - Supervised learning: Classification techniques - Model selection criteria and trade-offs - Supervised learning: Regression techniques - Unsupervised learning: Clustering algorithms |
| Evaluate Business Problem and Ethical Considerations | 20% | - Apply AI design thinking and AI Ladder framework - Assess ethical, legal, and compliance implications - Understand business requirements and objectives - Identify available data sources and constraints |
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