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| Section | Weight | Objectives |
|---|---|---|
| Model Deployment | 5-10% | - Model scoring and operationalization - Registering and publishing models - Exporting score code |
| Overview of Supervised Machine Learning | 10-15% | - Basic concepts and terminology - Prediction types and modeling goals - Model overfitting, underfitting, and generalization |
| Creating and Managing Pipelines | 15-20% | - Configuring and connecting nodes - Managing pipeline flow and execution - Using pipeline templates and automation - Building pipelines in Model Studio |
| Building Predictive Models | 25-30% | - Decision trees and tree ensembles - Regularization and optimization methods - Using appropriate modeling nodes - Neural networks and deep learning basics - Regression models (linear, logistic) |
| Model Assessment and Comparison | 15-20% | - Interpreting model results and diagnostics - Regression metrics: RMSE, R-squared, MAE - Profit/loss analysis and cutoff adjustment - Comparing models and selecting best performer - Classification metrics: accuracy, precision, recall, F1, AUC |
| Data Preparation and Exploration | 20-25% | - Partitioning data into training, validation, and test sets - Variable selection and reduction - Feature engineering and transformation - Data profiling and exploration - Loading and accessing data sources - Handling missing values and outliers |
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