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DTEL1 - 1 Welcome to DTE class

DTEL1 - 2 Class Introduction

DTEL1 - 3 Introduction to Decision Trees

DTEL1 - 4 Impurity Functions

DTEL1 - 5 CART Algorithm

DTEL1 - 6 Basic Properties of Decision Trees

DTEL1 7 Basic Regularization of Trees

DTEL1 - 8 Sklearn Trees

DTEL1 - 9 Conclusion

DTEL2 2 1 Introduction

DTEL2 2 2 Bias variance trade off

DTEL2 2 3 Bias variance Decomposition

DTEL2 2 4 Generalizations of bias variance tradeoff

DTEL2 2 5 ExtraTrees Algorithm

DTEL2 2 6 ExtraTrees with Sklearn

DTEL2 2 7 Conclusion

DTEL3 3 1 Introduction

DTEL3 3 2 Bootstrap

DTEL3 3 3 Bagging

DTEL3 3 4 1 Example

DTEL3 3 5 Random Forest

DTEL3 3 4 2 Example Notebook

DTEL3 3 6 General Ensembling

DTEL4 4 1 Introduction

DTEL4 4 2 Proximities

DTEL4 4 3 Proximities Visualizations

DTEL4 4 4 Feature Importance's

DTEL4 4 5 Limitations of Tree Feature Importance

DTEL4 4 6 Feature Importance's in Random Forest

DTEL4 4 7 Summary

DTEL5 5 1 Introduction

DTEL5 5 2 Boosting

DTEL5 5 3 Gradient Boosting

DTEL5 5 4 XGBoost

DTEL5 5 5 LightGBM

DTEL5 5 6 CatBoost

Description:

Dive into the world of decision trees and ensemble methods in this comprehensive 2.5-hour course. Explore fundamental concepts such as impurity functions, CART algorithm, and basic properties of decision trees. Learn about regularization techniques and how to implement decision trees using Scikit-learn. Delve into advanced topics like bias-variance trade-off, ExtraTrees algorithm, bootstrap, bagging, and random forests. Discover the power of ensemble methods, including proximities, feature importance, and various boosting techniques such as gradient boosting, XGBoost, LightGBM, and CatBoost. Gain practical experience through examples and notebook exercises, equipping you with the skills to apply these powerful machine learning techniques to real-world problems.

Decision Trees and Ensemble Methods

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#Computer Science #Machine Learning #Ensemble Learning #Data Science #Predictive Modeling #Decision Trees #Random Forests #Ensemble Methods #Bagging

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