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IBM C1000-144 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Refine and Deploy the Model | 18% | - Model explainability and interpretability - Hyperparameter optimization - Feature engineering and selection - Environment configuration and training setup - Use IBM Watson Studio and related tools |
| Implement the Proper Machine Learning Model | 26% | - Unsupervised learning: Dimensionality reduction - Model selection and validation - Supervised learning: Regression - Supervised learning: Classification - Unsupervised learning: Clustering |
| Exploratory Data Analysis and Preparation | 18% | - Data balancing, partitioning, and transformation - Data cleaning, labeling, and anonymization - Data visualization and statistical analysis |
| Evaluate Business Problem Including Ethical Implications | 21% | - Understand business requirements - Apply AI design thinking - Identify ethical and compliance challenges - Follow the AI Ladder methodology - Assess available data and feasibility |
| Monitor Models in Production | 17% | - Model performance assessment - Production monitoring and drift detection - Bias and fairness evaluation - Maintenance and retraining strategy |
