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Course Curriculum

Welcome
Learning Objectives Details 00:00:00
Ensemble methods
Introduction to Ensemble Methods Details 00:03:00
Benefits of Ensemble Methods Details 00:04:00
Randomized Representation Details 00:09:00
Building Multiple Models Details 00:04:00
Binomial Distribution
Introduction To Binomial Distribution Details 00:04:00
Coin Example Details 00:09:00
The Formula Details 00:04:00
Example Problem Details 00:05:00
Applications Details 00:05:00
Why Does It Work? Details 00:06:00
Bagging
Introduction to Bagging Details 00:06:00
How Bagging Works? Details 00:05:00
Sampling With Replacement Details 00:05:00
Why Is Bagging Useful Details 00:13:00
Effects And Vulnerabilities Details 00:05:00
Random Forest
Introduction To Random Forests Details 00:06:00
How Do Random Forests Work? Details 00:13:00
Features Used For Learning Details 00:15:00
Titanic Data Set Example–Building A Model Details 00:14:00
Titanic Data Set Example — Training Model Details 00:11:00
Titanic Data Set Example — Tuning Parameters Details 00:10:00
Using The Random Forest Package Details 00:22:00
Building Binary Classification Model: Adult Census Income Dataset Details 01:00:00
Boosting
Building Binary Classification Models With Boosted Decision Trees: Titanic Dataset Details 00:45:00
Building Binary Classification Models With Boosted Decision Trees: Titanic Dataset (Python) Details 00:45:00
Supplementary Exercises
Feature Engineering and Variable Importance Calculation Details 00:30:00
Extracting Titles from Titanic Dataset Details 00:45:00

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