Course curriculum
-
1
Bagging
- Resources to be used in this course
- Problem Statement
- Understanding Ensemble Learning
- Introducing Bagging Algorithms
- Hands-on to Bagging Meta Estimator
- Introduction to Random Forest
- Understanding Out-Of-Bag Score
- Random Forest VS Classical Bagging VS Decision Tree
- Project
-
2
Boosting
- Introduction to Boosting
- AdaBoost Step-by-Step Explanation
- Hands-on - AdaBoost
- Gradient Boosting Machines (GBM)
- Hands-on Gradient Boost
- Other Algo (XGBoost, LightBoost. CatBoost)
- Project: Anova Insurance
Bagging and Boosting ML Algorithms
This course will provide you with a hands-on understanding of Bagging and Boosting techniques in machine learning. By the end of the course, you will be proficient in implementing and tuning these ensemble methods to enhance model performance. You'll learn to apply algorithms like Random Forest, AdaBoost, and Gradient Boosting to a real-world dataset, equipping you with the skills to improve predictive accuracy and robustness in your projects.
Who Should Enroll:
Professionals: Individuals looking to deepen their knowledge and apply advanced machine learning techniques like Bagging and Boosting to solve complex problems across various domains
Aspiring Students: Individuals looking to deepen their knowledge and apply advanced ML techniques to bring value to businesses
Key Takeaways from the Course
-
Lear to effectively use Bagging and Boosting Algorithms
-
Hands-On Experience: Engage in practical exercises to reinforce learning and apply concepts, ensuring you gain the skills to utilize these algorithms