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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