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Introduction

Machine Learning Concepts

🎥 Introducing machine-learning concepts
✅ Quiz Intro.01

The predictive modeling pipeline

Module overview
Tabular data exploration
Fitting a scikit-learn model on numerical data

Categorical data

Handling categorical data

Decision tree models

Module overview
Intuitions on tree-based models
- 🎥 Intuitions on tree-based models
- ✅ Quiz M5.01

Combining categorical and numerical data

Combining categorical and numerical data

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

Advanced topics

Module overview
🎥 Advanced topics
Resources for learning about advanced topics

Concluding remarks

🎥 Concluding remarks
Concluding remarks

Appendix

Glossary
Datasets description
Acknowledgement
Notebook timings
Table of contents

Repository
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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

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✅ Quiz M5.01

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🎥 Visualizing scikit-learn pipelines in Jupyter

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