Advanced Certificate in Modern Evaluation Criteria
-- ViewingNowThe Advanced Certificate in Modern Evaluation Criteria is a comprehensive course designed to equip learners with the latest evaluation techniques and methodologies. This certification focuses on the importance of data-driven decision making and performance measurement in today's fast-paced business environment.
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⢠Advanced Evaluation Metrics: Comprehensive study of modern evaluation techniques and metrics, including accuracy, precision, recall, F1-score, ROC-AUC, and log loss.
⢠Statistical Analysis in Evaluation: Introduction to statistical methods and their application in evaluating machine learning models and algorithms.
⢠Cross-Validation Techniques: Study of various cross-validation techniques, such as k-fold cross-validation, stratified cross-validation, and leave-one-out cross-validation.
⢠Bias-Variance Tradeoff: Understanding the concept of bias-variance tradeoff and its impact on model evaluation and selection.
⢠Evaluation of Deep Learning Models: In-depth analysis of evaluation metrics and techniques specific to deep learning models.
⢠Evaluation of Natural Language Processing Models: Examination of evaluation metrics and techniques for natural language processing models, including BLEU, ROUGE, and perplexity.
⢠Evaluation of Time Series Models: Study of evaluation metrics and techniques for time series models, such as MAE, RMSE, and NRMSE.
⢠Model Selection and Hyperparameter Tuning: Techniques and best practices for selecting the best model and hyperparameter tuning using techniques such as Grid Search, Random Search, and Bayesian Optimization.
⢠Evaluation of Imbalanced Datasets: Understanding the challenges of evaluating models trained on imbalanced datasets and techniques for addressing them, such as Precision-Recall curves, PR-AUC, and Cohen's Kappa.
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