ML: Automated Machine Learning
summary
Covers automated machine learning (AutoML) methods for tasks like feature transformation, variable selection, model selection and hyperparameter tuning for algorithms such as RandomForest and XGBoost. Explains how AutoML differs from classical manual approaches and demonstrates using cloud-based AutoML solutions to optimize models via cross-validation for business problems.
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chapters (6)
- 0:00Introduction to AutoML
- 3:26What is machine learning
- 10:13AutoML applications
- 16:15Model parameter tuning
- 22:16Tools and examples
- 28:48Summary
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