This course provides a comprehensive understanding of confusion matrix and its application in AI and machine learning model evaluation. Participants will explore topics such as binary classification, multi-class classification, ROC curve analysis, precision-recall curve analysis, threshold adjustment, handling imbalanced datasets, advanced evaluation techniques, challenges in model evaluation, explainability, domain-specific applications, and future trends in model evaluation.

Who This Course Is For:

This course is designed for data scientists, machine learning engineers, AI researchers, and professionals working in the field of artificial intelligence and machine learning. It is also suitable for students pursuing studies in data science, computer science, or related fields who want to deepen their understanding of model evaluation techniques. Additionally, professionals involved in model deployment, evaluation, and optimization will find this course beneficial for enhancing their skills in assessing model performance. Whether you are a beginner or an experienced practitioner, this course equips you with the knowledge and tools necessary to effectively evaluate AI and machine learning models using confusion matrix and related techniques.