Ensemble Machine Learning in Python: Random Forest, AdaBoost

Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python

Created by: Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

Created by: Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

What Will I Learn?

• Understand and derive the bias-variance decomposition
• Understand the bootstrap method and its application to bagging
• Understand why bagging improves classification and regression performance
• Understand and implement Random Forest
• Understand and implement AdaBoost

Requirements

• Calculus (derivatives)
• Numpy, Matplotlib, Sci-Kit Learn
• K-Nearest Neighbors, Decision Trees
• Probability and Statistics (undergraduate level)
• Linear Regression, Logistic Regresion

Target audience

• Understand the types of models that win machine learning contests (Netflix prize, Kaggle)
• Students studying machine learning
• Professionals who want to apply data science and machine learning to their work
• Entrepreneurs who want to apply data science and machine learning to optimize their business
• Students in computer science who want to learn more about data science and machine learning
• Those who know some basic machine learning models but want to know how today's most powerful models (Random Forest, AdaBoost, and other ensemble methods) are built