Global Certificate in Smart Model Selection Techniques
-- ViewingNowThe Global Certificate in Smart Model Selection Techniques is a comprehensive course designed to empower learners with the essential skills needed to excel in today's data-driven world. This course focuses on the importance of selecting the right model for the right problem, a critical skill for data scientists and analysts.
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โข Introduction to Smart Model Selection Techniques: Overview of model selection, its importance, and different techniques.
โข Understanding Data Preprocessing: Data cleaning, normalization, transformation, and feature selection.
โข Model Evaluation Metrics: Accuracy, precision, recall, F1 score, ROC curve, and AUC.
โข Cross-Validation Techniques: K-fold cross-validation, stratified cross-validation, and time series cross-validation.
โข Regularization Techniques: L1 and L2 regularization, and their impact on model selection.
โข Ensemble Methods: Bagging, boosting, and stacking for smart model selection.
โข Hyperparameter Tuning: Grid search, random search, and Bayesian optimization.
โข Automated Machine Learning: Overview of AutoML, its benefits, and limitations in model selection.
โข Real-World Challenges: Addressing data imbalance, missing data, and high-dimensional data in model selection.
โข Best Practices: Documentation, code versioning, and collaboration in smart model selection.
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