Table of contents

• Introduction

Setting the stage

• 🎥 Setting the stage

Machine Learning Concepts

• 🎥 Introducing machine-learning concepts
• ✅ Quiz Intro.01

The predictive modeling pipeline

• Module overview
• Tabular data exploration
  • 🎥 Tabular data exploration
  • 📝 Exercise M1.01
  • 📃 Solution for Exercise M1.01
• Fitting a scikit-learn model on numerical data
  • First model with scikit-learn
  • 📝 Exercise M1.02
  • 📃 Solution for Exercise M1.02
• Data preprocessing
  • Preprocessing numerical features
  • 📝 Exercise M1.03
  • 📃 Solution for Exercise M1.03
  • Preprocessing categorical features
  • 📝 Exercise M1.04
  • 📃 Solution for Exercise M1.04
  • Using numerical and categorical features together
• Wrap-up
  • Main take-away

Selecting the best model

• Module overview
• Overfitting and underfitting
  • 🎥 Overfitting and Underfitting
  • Cross-validation framework
  • ✅ Quiz M2.01
• Validation and learning curves
  • 🎥 Comparing train and test errors
  • Overfit-generalization-underfit
  • 📝 Exercise M2.01
  • 📃 Solution for Exercise M2.01
  • ✅ Quiz M2.02
• Bias versus variance trade-off
  • 🎥 Bias versus Variance
  • ✅ Quiz M2.03
• 🏁 Wrap-up quiz 2
• Main take-away

Hyperparameter tuning

• Module overview
• Manual tuning
  • Set and get hyperparameters in scikit-learn
  • ✅ Quiz M3.01
• Automated tuning
  • Hyperparameter tuning by grid-search
  • Evaluation and hyperparameter tuning
  • 📝 Exercise M3.02
  • 📃 Solution for Exercise M3.02
  • ✅ Quiz M3.02
• 🏁 Wrap-up quiz 3
• Main take-away

Final exercise

• 📝 Exercise: Try out learned skills on Ames Housing dataset

Concluding remarks

• Concluding remarks

Appendix

• Glossary
• Datasets description
  • The adult census dataset
  • The California housing dataset
  • The Ames housing dataset
• Acknowledgement
• Notebook timings
• Table of contents