Ensemble Learning

What you'll be able to do

• What is ensemble method and why is it so effecient?
• Learn various types of ensemble methods
• Bias Variance tradeoff using ensemble method
• Build your first bagging model - Random Forest
• Understand Gradient Boosting, XGBoost etc
• Tune the hyperparameters to improve the performance
• Interpret the complex ensemble models
• Use all cores of laptop/pc to train models fast

1. 01Section 1 : Introduction to Ensemble Models3 lessons · 7 min

   1. Introduction to Ensembles2:22
   2. Types of Ensembles3:33
   3. Download Resources1:00
2. 02Section 2 : Bagging Ensembles8 lessons · 37 min

   1. Bootstrap Aggregation6:17
   2. How Bagging Reduces Variance4:29
   3. Random Forest Algo9:13
   4. Out of Bag Cross validation2:19
   5. Bias Variance Tradeoff for Random Forests2:40
   6. Hyperparameters7:22
   7. Warm Start3:21
   8. Course Review1:00
3. 03Section 3 : Extremely Randomised Trees1 lesson · 9 min

   1. Extremely Randomised Trees8:42
4. 04Section 4 : Gradient Boosing Algorithm5 lessons · 42 min

   1. Boosting Main Ideas5:45
   2. Boosting Intuition12:35
   3. Pseudo Residuals for Gradient Boosting9:40
   4. Gradient Boosting Algorithm8:43
   5. Controlling the learning rate5:17
5. 05Section 5 : Adaboost2 lessons · 14 min

   1. AdaBoost Intuition and Formulaa6:23
   2. Adaboost Visual Example7:50
6. 06Section 6 : Stacking Ensembles2 lessons · 12 min

   1. Stacking based ensembles5:20
   2. Stacking Code Demo6:13
7. 07Section 7 : Cascading Ensembles2 lessons · 14 min

   1. Cascading Ensembles7:20
   2. Cascading Models Special Cases6:47
8. 08Section 8 : Cohorted Ensembles1 lesson · 15 min

   1. Cohorted Ensembles14:31
9. 09Section 9 : XGBoost2 lessons · 12 min

   1. Extreme Gradient Boosting4:25
   2. XGBoost Demo7:43
10. 10Section 10 : CatBoost1 lesson · 5 min

    1. CatBoost5:04
11. 11Section 11 : Light GBM1 lesson · 8 min

    1. LightGBM7:39

What you need

• Basics of Python
• Foundational knowledge of Data Science
• High school maths

Who this course is for

• Data Science Aspirants
• Data Science Professionals
• Software/Data engineers interested in quantitative analysis
• Professionals working with large datasets
• Data analysts, economists, researchers

Your instructor

Prabhakaran S

Principal Data Scientist

My name is Selva, and I am super excited to mentor you on this course!

I head the Data Science team for a global Fortune 500 company and over the last 10 years of my data science experience I've deployed 20+ global products. I'm also the Founder and Chief Author of Machine Learning Plus, which has over 4M annual readers.

I specialize in covering the in-depth intuition and maths of any concept or algorithm. And based on my existing student requests, I've put up the series of courses and projects with detailed explanations, just like on-the-job experience. Hope you love it!

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