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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