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Course Outline
Introduction
- Overview of Random Forest features and advantages
- Understanding decision trees and ensemble methods
Getting Started
- Setting up the libraries (Numpy, Pandas, Matplotlib, etc.)
- Classification and regression in Random Forests
- Use cases and examples
Implementing Random Forest
- Preparing data sets for training
- Training the machine learning model
- Evaluating and improving accuracy
Tuning the Hyperparameters in Random Forest
- Performing cross-validations
- Random search and Grid search
- Visualizing training model performance
- Optimizing hyperparameters
Best Practices and Troubleshooting Tips
Summary and Next Steps
Requirements
- An understanding of machine learning concepts
- Python programming experience
Audience
- Data scientists
- Software engineers
14 Hours
