Global Certificate in ML Model Enhancement Techniques
-- ViewingNowThe Global Certificate in ML Model Enhancement Techniques course is a comprehensive program designed to empower learners with the latest techniques in machine learning model enhancement. This course is critical for professionals seeking to stay updated with industry demands and advance their careers in AI and machine learning.
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โข Machine Learning Model Enhancement Fundamentals: Understanding the basics of machine learning model enhancement techniques, including model evaluation, selection, and optimization.
โข Data Preprocessing: Techniques for data cleaning, normalization, and feature engineering to improve model performance.
โข Feature Selection and Dimensionality Reduction: Methods for selecting relevant features and reducing the dimensionality of data to improve model performance and interpretability.
โข Ensemble Methods: Techniques for combining multiple models to improve predictive accuracy, including bagging, boosting, and stacking.
โข Hyperparameter Tuning: Strategies for optimizing model hyperparameters, including grid search, random search, and Bayesian optimization.
โข Regularization Techniques: Methods for preventing overfitting and improving model generalization, including L1 and L2 regularization.
โข Transfer Learning and Domain Adaptation: Approaches for adapting pre-trained models to new domains and tasks.
โข Interpretability and Explainability: Techniques for understanding and interpreting model predictions, including feature importance, partial dependence plots, and LIME.
โข Evaluation Metrics: Methods for evaluating model performance, including accuracy, precision, recall, F1 score, and ROC curve.
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