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IBM A1000-144 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitor and Maintain Models in Production | 10% | - Assess model performance and accuracy - Update and retrain models as needed - Identify and mitigate bias and fairness issues - Detect model drift and data drift |
| Topic 2: Exploratory Data Analysis and Data Preparation | 25% | - Perform statistical analysis and data visualization - Handle missing values and outliers - Clean, label, anonymize, and transform data - Balance, partition, and split datasets |
| Topic 3: Select and Implement Machine Learning Models | 25% | - Unsupervised learning: Clustering algorithms - Supervised learning: Classification techniques - Unsupervised learning: Dimensionality reduction - Supervised learning: Regression techniques - Model selection criteria and trade-offs |
| Topic 4: Evaluate Business Problem and Ethical Considerations | 20% | - Apply AI design thinking and AI Ladder framework - Understand business requirements and objectives - Assess ethical, legal, and compliance implications - Identify available data sources and constraints |
| Topic 5: Refine, Optimize and Deploy Models | 20% | - Feature engineering and feature selection - Prepare environment for model deployment - Use IBM Watson Studio and related tools - Hyperparameter tuning and model optimization - Model explainability and interpretability |
