This course is intended for data science practitioners who work with Azure Machine Learning Service and who seek to improve their ML model accuracy, efficiency, and explainability.
Data science and machine learning professionals work tirelessly to improve the quality of their ML models. In this course, Evaluating Model Effectiveness in Microsoft Azure, you will learn how to use Azure Machine Learning Studio to improve your models. First, you will learn how to evaluate model effectiveness in Azure. Next, you will discover how to improve model performance by eliminating overfitting and implementing ensembling. Finally, you will explore how to assess ML model interpretability. When you are finished with this course, you will have the skills and knowledge of Azure Machine Learning needed to ensure your ML models are consistent, accurate, and explainable.
Data science and machine learning professionals work tirelessly to improve the quality of their ML models. In this course, Evaluating Model Effectiveness in Microsoft Azure, you will learn how to use Azure Machine Learning Studio to improve your models. First, you will learn how to evaluate model effectiveness in Azure. Next, you will discover how to improve model performance by eliminating overfitting and implementing ensembling. Finally, you will explore how to assess ML model interpretability. When you are finished with this course, you will have the skills and knowledge of Azure Machine Learning needed to ensure your ML models are consistent, accurate, and explainable.
