Book Content
chapters • 11h12m total length
1. Get Closer to Your Data with Exploratory Data Analysis
2. Getting Started with Ensemble Machine Learning
3. Resampling Methods
4. Statistical & Machine Learning Algorithms
5. Bag the Models with Bagging
6. When in Doubt, use Random Forest
7. Boost up Model Performance with Boosting
8. Blend it with Stacking
9. Homogeneous Ensemble for Hand-Written Digits Recognition
10. Heterogeneous Ensemble Classifiers for Credit Card Default Prediction
11. Heterogeneous Ensemble for Sentiment Analysis using NLP
12. Heterogeneous Ensemble for Multi-Label Classification for Text Categorization
