First, you'll explore the fundamental principles of Random Forest, including how it leverages
ensemble learning by combining multiple decision trees to enhance accuracy, reduce variance, and improve predictive performance. Next, you'll discover key techniques such as feature importance, hyperparameter tuning, and strategies to prevent overfitting. Finally, you'll learn how to implement Random Forest using Python and scikit-learn, applying it to real-world datasets. When you're finished with this course, you'll have the skills and knowledge of Random Forest needed to develop reliable and high-performing machine learning models.
Homepage:

https://www.pluralsight.com/courses/random-forests