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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 required libraries (Numpy, Pandas, Matplotlib, etc.)
- Classification and regression using Random Forests
- Real-world use cases and examples
Implementing Random Forest
- Preparing datasets for training
- Training the machine learning model
- Evaluating and improving model accuracy
Tuning Hyperparameters in Random Forest
- Performing cross-validation
- Random search and Grid search techniques
- Visualizing training model performance
- Optimizing hyperparameters
Best Practices and Troubleshooting Tips
Summary and Next Steps
Requirements
- A foundational understanding of machine learning concepts
- Experience with Python programming
Target Audience
- Data scientists
- Software engineers
14 Hours
