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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
Data preprocessing
Wrap-up
- Main take-away

Selecting the best model

Module overview
Overfitting and underfitting
Validation and learning curves
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
🏁 Wrap-up quiz 3
Main take-away

Concluding remarks

🎥 Concluding remarks
Concluding remarks

Appendix

Glossary
Datasets description
Acknowledgement
Notebook timings
Table of contents

Repository
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Validation and learning curves

Validation and learning curves#

🎥 Comparing train and test errors
Overfit-generalization-underfit
📝 Exercise M2.01
📃 Solution for Exercise M2.01
✅ Quiz M2.02

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✅ Quiz M2.01

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🎥 Comparing train and test errors

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