AI/ML Model Evaluation and Validation in Machine Learning скачать в хорошем качестве

AI/ML Model Evaluation and Validation in Machine Learning

-- For the full video go to: https://www.oreilly.com/live-events/a... In today's tutorial, I will dive into the critical aspects of model evaluation and validation in Machine Learning (ML) and Artificial Intelligence (AI). This video is a detailed exploration aimed at providing a nuanced understanding of how to assess and validate ML models effectively. As we venture into the intricacies of model evaluation and validation, I'll share insights and methodologies crucial for anyone in the AI and ML domain, from academic researchers to industry practitioners. This tutorial is not just about theory; it's a practical guide grounded in the latest research and best practices. Fundamentals of Model Evaluation: We'll start with the fundamentals, exploring the key concepts and metrics used in model evaluation. This includes accuracy, precision, recall, F1 score, and ROC-AUC. I'll explain the significance of each metric and when to use them, ensuring a comprehensive understanding of model performance. Validation Techniques: Moving on, I'll delve into various model validation techniques such as cross-validation, bootstrap, and holdout methods. I'll discuss the advantages and limitations of each approach and how they can be effectively implemented in different scenarios. Overfitting and Underfitting: A critical part of model evaluation is understanding and addressing the issues of overfitting and underfitting. I'll explain these concepts with examples and demonstrate mitigating techniques, such as regularisation and ensemble methods. Advanced Evaluation Strategies: I'll also cover advanced evaluation strategies like confusion matrix analysis, learning curves, and performance trade-offs. This segment is essential for fine-tuning their models to achieve optimal performance. Ethical Considerations in Model Evaluation: In keeping with responsible AI practices, I'll highlight the importance of ethical considerations in model evaluation. This includes ensuring fairness, avoiding bias, and maintaining transparency in AI models. By the end of this tutorial, you'll have a robust understanding of model evaluation and validation techniques, enabling you to assess and improve your ML models with confidence and precision. Subscribe to "AI Security Oxford" for more in-depth explorations into AI and ML. I welcome your comments, questions, and discussions below—let’s engage and grow our understanding together! #AISecurity #ModelEvaluation #ModelValidation #MachineLearning #ArtificialIntelligence #DataScience