Table of contents

• Introduction

Machine Learning Concepts

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

The predictive modeling pipeline

• Module overview
• Tabular data exploration
  • First look at our dataset
  • 📝 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
  • Working with numerical data
  • 📝 Exercise M1.03
  • 📃 Solution for Exercise M1.03
  • Preprocessing for numerical features
  • 🎥 Validation of a model
  • Model evaluation using cross-validation
  • ✅ Quiz M1.02

Categorical data

• Handling categorical data
  • Encoding of categorical variables
  • 📝 Exercise M1.04
  • 📃 Solution for Exercise M1.04
  • Using numerical and categorical variables together

Combining categorical and numerical data

• Combining categorical and numerical data
  • 🎥 Visualizing scikit-learn pipelines in Jupyter
  • Visualizing scikit-learn pipelines in Jupyter
  • ✅ Quiz M1.03
  • 🏁 Wrap-up quiz 1
  • Main take-away

Selecting the best model

• Module overview
• 🎥 Overfitting and Underfitting
• ✅ Quiz M2.01
• 🎥 Comparing train and test errors
• 📝 Exercise M2.01
• 📃 Solution for Exercise M2.01

Concluding remarks

• 🎥 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