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Course Outline
Introduction
- Overview of Random Forest features and benefits.
- Understanding decision trees and ensemble methods.
Getting Started
- Setting up libraries (Numpy, Pandas, Matplotlib, etc.).
- Applying classification and regression in Random Forests.
- Exploring use cases and examples.
Implementing Random Forest
- Preparing datasets for training.
- Training the machine learning model.
- Evaluating and improving accuracy.
Tuning Hyperparameters in Random Forest
- Conducting cross-validations.
- Utilizing random search and grid search techniques.
- Visualizing training model performance.
- Optimizing hyperparameters.
Best Practices and Trouleshooting Tips
Summary and Next Steps
Requirements
- A foundational understanding of machine learning concepts.
- Experience with Python programming.
Audience
- Data scientists.
- Software engineers.
14 Hours
