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
Testimonials (4)
Keeping it short and simple. Creating intuition and visual models around the concepts (decision tree graph, linear equations, calculating y_pred manually to prove how the model works).
Nicolae - DB Global Technology
Course - Machine Learning
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Course - Azure Machine Learning (AML)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete