Bagging and Boosting ML Algorithms
AdvancedLevel
1 hrs 0 minsDuration
About this Course
- Learn the fundamentals of Bagging and Boosting techniques in machine learning, including how they improve predictive accuracy and robustness in model performance.
- Gain practical experience by implementing popular algorithms like Random Forest and AdaBoost, applying them to real-world datasets to see their effectiveness in action.
- Discover how to combine multiple models using ensemble methods to enhance predictive performance, ensuring accurate and reliable results in machine learning projects.
Learning Outcomes
Bagging & Boosting
Understand Bagging and Boosting technique to improve model performance
Master Ensemble Methods
Implement and tune ensemble methods like Random Forest, AdaBoost etc.
Practical Application
Reinforce learning with practical exercises on Bagging and Boosting.
Who Should Enroll
- For professionals looking to apply Bagging and Boosting to solve complex real-world problems.
- Ideal for students eager to learn advanced ML techniques and apply them in business contexts.
- Perfect for anyone interested in mastering Bagging and Boosting methods for industry applications.
Course Curriculum
Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.
1. Resources to be used in this course
2. Problem Statement
3. Understanding Ensemble Learning
4. Introducing Bagging Algorithms
5. Hands-on to Bagging Meta Estimator
6. Introduction to Random Forest
7. Understanding Out-Of-Bag Score
8. Random Forest VS Classical Bagging VS Decision Tree
9. Project Hands On
1. Introduction to Boosting
2. AdaBoost Step-by-Step Explanation
3. Hands-on - AdaBoost
4. Gradient Boosting Machines (GBM)
5. Hands-on Gradient Boost
6. Other Algo (XGBoost, LightBoost. CatBoost)
7. Project: Anova Insurance
Meet the instructor
Our instructor and mentors carry years of experience in data industry
Get this Course Now
With this course you’ll get
- 90 hour
Duration
- Kunal Jain, Apoorv Vishnoi
Instructor
- Advanced
Level
Certificate of completion
Earn a professional certificate upon course completion
- Globally recognized certificate
- Verifiable online credential
- Enhances professional credibility
Frequently Asked Questions
Looking for answers to other questions?
Bagging (Bootstrap Aggregating) and Boosting are ensemble learning techniques that combine multiple weak models to create a strong predictive model. Bagging reduces variance, while Boosting focuses on reducing bias by iteratively improving model performance.
Random Forest is a Bagging-based ensemble method that creates a forest of decision trees, where each tree is trained on a random subset of data. The final prediction is based on the majority vote of all trees.
AdaBoost (Adaptive Boosting) is a Boosting algorithm that combines weak learners (usually decision trees) to form a strong learner. It adjusts the weights of misclassified instances to focus more on hard-to-predict data points.
Yes, both Bagging and Boosting are widely used in business for tasks like customer segmentation, fraud detection, and predictive analytics to improve model performance and accuracy.
You will learn to evaluate model performance using metrics like accuracy, precision, recall, and F1-score. Additionally, techniques like cross-validation will be used to assess the robustness of your models.
Related courses
Expand your knowledge with these related courses and expand way beyond
1 Hours6 Lessons4.6
Getting Started with Large Language Models
0 Hours1 Lessons4.8
Navigating LLM Tradeoffs: Techniques for Speed, Cost, Scale & Accuracy
1 Hours6 Lessons4.5
Framework to Choose the Right LLM for Your Business
Popular Categories
Discover our most popular courses to boost your skills
Popular free courses
Discover our most popular courses to boost your skills
Contact Us Today
Take the first step towards a future of innovation & excellence with Analytics Vidhya
Unlock Your AI & ML Potential
Get Expert Guidance
Need Support? We’ve Got Your Back Anytime!
+91-8068342847 | +91-8046107668
10AM - 7PM (IST) Mon-Sun[email protected]
You'll hear back in 24 hours