Ensemble Learning and Ensemble Learning Techniques

  • AdvancedLevel

  • 813+Students Enrolled

  • 30 MinsDuration

  • 4.8Average Rating

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About this Course

  • Learn key ensemble learning techniques, including bagging, boosting, and stacking, to enhance machine learning models.
  • Gain real-world experience with Python-based case studies, applying ensemble methods to improve model accuracy and performance.
  • Explore how ensemble methods combine multiple models to create more robust and accurate machine learning predictions.

Learning Outcomes

Master Ensemble Learning

Learn the fundamentals, and techniques of ensemble learning in ML.

Deep Dive into ML

Explore bagging, boosting, and stacking to enhance model accuracy.

Hands-On Ensemble Skills

Apply ensemble learning skills & methods on real datasets.

Who Should Enroll

  • Ideal for those looking to master Ensemble Learning for machine learning and data classification.
  • Perfect for individuals seeking to enhance their classification skills and apply Ensemble learning in real-world.
  • Suitable for those starting their ML journey and wanting to learn probability-based classification techniques.

Course Curriculum

Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.

tools

  1. 1. Intuition behind Ensemble Learning

  2. 2. What is Ensemble Learning?

  3. 3. What models will be covered in the course?

  4. 4. Quiz: Introduction to Ensemble Learning

  5. 5. AI&ML Blackbelt Plus Program (Sponsored)

  1. 1. Max Voting Technique

  2. 2. Averaging Technique

  3. 3. Weighted Average

  4. 4. Quiz: Basic Ensemble Techniques

  1. 1. Stacking Technique

  2. 2. Implementing Stacking

  3. 3. Variants of Stacking

  4. 4. Blending Ensemble Technique

  5. 5. Bootstrap Sampling

  6. 6. Quiz: Bootstrap Sampling

  1. 1. What is Bagging?

  2. 2. Bagging Meta-Estimator

  3. 3. Random Forest Technique

  4. 4. Quiz: Random Forest

  5. 5. Hyper-parameters of Random Forest

  6. 6. Quiz: Hyper-parameters of Random Forest

  7. 7. Implementing Random Forest

  1. 1. Introduction to boosting

  2. 2. What is Boosting?

  3. 3. Quiz: Introduction to Boosting

  4. 4. Gradient Boosting Algorithm (GBM)

  5. 5. Quiz: Gradient Boosting Algorithm

  6. 6. Extreme Gradient Boosting (XGBoost)

  7. 7. Implementing XGBoost

  8. 8. Quiz: XGBoost Technique

  9. 9. AdaBoost: Adaptive Boosting

  10. 10. Implementing AdaBoost

  11. 11. Quiz: AdaBoost Technique

  12. 12. LightGBM Technique

  13. 13. Exploring CatBoost

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.

Get this Course Now

With this course you’ll get

  • 30 Mins

    Duration

  • Kunal Jain

    Instructor

  • Advanced

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Globally recognized certificate
  • Verifiable online credential
  • Enhances professional credibility
certificate

Frequently Asked Questions

Looking for answers to other questions?

This course is designed for anyone who wants to understand how Ensemble Learning and its various techniques work. It's especially beneficial for those looking to expand their machine learning skillset.​

Yes, a basic grasp of machine learning algorithms like decision trees and random forests is recommended. If you're new to these concepts, consider enrolling in our "Getting Started with Decision Trees" free course first.​

This course is free of cost!

The course includes theoretical explanations, practical case studies, and hands-on coding exercises to help learners grasp ensemble learning techniques effectively.

Yes, you will receive a certificate of completion after successfully finishing the course and assessments.

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