Bagging and Boosting ML Algorithms

  • AdvancedLevel

  • 668+Students Enrolled

  • 1 HrDuration

  • 4.5Average Rating

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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.

tools

  1. 1. Resources to be used in this course

  2. 2. Problem Statement

  3. 3. Understanding Ensemble Learning

  4. 4. Introducing Bagging Algorithms

  5. 5. Hands-on to Bagging Meta Estimator

  6. 6. Introduction to Random Forest

  7. 7. Understanding Out-Of-Bag Score

  8. 8. Random Forest VS Classical Bagging VS Decision Tree

  9. 9. Project Hands On

  1. 1. Introduction to Boosting

  2. 2. AdaBoost Step-by-Step Explanation

  3. 3. Hands-on - AdaBoost

  4. 4. Gradient Boosting Machines (GBM)

  5. 5. Hands-on Gradient Boost

  6. 6. Other Algo (XGBoost, LightBoost. CatBoost)

  7. 7. Project: Anova Insurance

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Kunal Jain

Founder & CEO, Analytics Vidhya

Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.

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Apoorv Vishnoi

Head-Traning, Analytics Vidhya

Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups and mentored start-ups at incubation cells.

Get this Course Now

With this course you’ll get

  • 1 Hour

    Duration

  • Kunal Jain

    Instructor

  • Advanced

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

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.

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