Performance measures and validation methods

Understand how the performance of machine learning models can be measured, evaluated, and improved. This course focuses on the essential methods used to assess predictive models and validate their effectiveness, ensuring that results are reliable and generalisable to new data. You will explore key evaluation metrics, validation techniques, and best practices for comparing and selecting machine learning models. 

What you will learn:

• Fundamental concepts of model evaluation and validation.  
• Performance metrics for classification and regression tasks.  
• Techniques for training, testing, and validating machine learning models.  
• Methods for detecting overfitting and underfitting.  
• Cross-validation and other approaches for reliable model assessment.  
• Strategies for comparing models and selecting the most appropriate solution. 

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