Course Outline


  • 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


  • An understanding of machine learning concepts
  • Python programming experience


  • Data scientists
  • Software engineers
  14 Hours


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