Professional Certificate in Advanced Evaluation Essentials
-- ViewingNowThe Professional Certificate in Advanced Evaluation Essentials is a comprehensive course designed to equip learners with critical skills in evaluation and strategic decision-making. This program is crucial in today's data-driven world, where the ability to analyze and interpret complex information is highly valued.
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⢠Advanced Evaluation Metrics: This unit will cover the various advanced evaluation metrics such as Precision@k, Recall@k, F1 score, ROC-AUC, Average Precision, and Log Loss.
⢠Evaluation Methodologies: This unit will focus on different evaluation methodologies like cross-validation, bootstrapping, and A/B testing. It will also cover various bias and variance issues in model evaluation.
⢠Evaluation Tools and Libraries: This unit will introduce different evaluation tools and libraries such as Scikit-learn, TensorFlow, Keras, and PyTorch. It will cover how to use these tools to evaluate models and interpret results.
⢠Model Interpretation and Explainability: This unit will cover various model interpretation and explainability techniques like SHAP, LIME, and TreeExplainer. It will also discuss the importance of model interpretability in business decision making.
⢠Evaluation in Natural Language Processing (NLP): This unit will focus on specific evaluation metrics and methodologies used in NLP, such as BLEU, NIST, METEOR, and ROUGE scores. It will also cover issues related to data bias and fairness in NLP evaluation.
⢠Evaluation in Computer Vision: This unit will cover evaluation metrics and methodologies specific to computer vision, such as Intersection over Union (IoU), Mean Average Precision (mAP), and Object Detection Quality (ODQ) metrics. It will also discuss the challenges of evaluating models in real-world scenarios.
⢠Evaluation in Time Series Analysis: This unit will cover evaluation metrics and methodologies specific to time series analysis, such as MAE, RMSE, and MAPE. It will also discuss the challenges of evaluating models in non-stationary and noisy environments.
⢠Evaluation in Deep Learning: This unit will cover evaluation metrics and methodologies specific to deep learning, such as accuracy, precision, recall, F1 score, and ROC-AUC. It will also discuss the challenges of evaluating deep learning models and hyperparameter tuning techniques.
⢠Evaluation in Reinforcement Learning: This unit will cover evaluation metrics and methodologies specific to reinforcement learning, such as return, discount
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