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Introduction
1. Python Basics
- Introduction to Python
- Working with DataFrames
2. Linear Models and Overfitting
3. Honest Estimates of Predictive Performance
- Training and Test Sets
- Cross-validation
4. Fighting Overfitting: the Advent of Machine Learning
5. Hyperparameter Optimization and Nested Cross-validation
- Cross-validation Revisited
- Nested Cross-validation
6. Generalizability, Validity, Fairness
7. Model Explanation
- Explaining “Classical” Models
- Black Boxes
8. Complex Models: from Ensembles to Deep Learning
- From Ensembles to Deep Learning
9. Scientific Examples
References

Index

By Tamas Spisak