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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
Handling categorical data
🏁 Wrap-up quiz 1
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
Automated tuning
🏁 Wrap-up quiz 3
Main take-away

Linear models

Module overview
Intuitions on linear models
Non-linear feature engineering for linear models
Regularization in linear model
🏁 Wrap-up quiz 4
Main take-away

Decision tree models

Module overview
Intuitions on tree-based models
- 🎥 Intuitions on tree-based models
- ✅ Quiz M5.01
Decision tree in classification
Decision tree in regression
Hyperparameters of decision tree
- Importance of decision tree hyperparameters on generalization
- ✅ Quiz M5.04
🏁 Wrap-up quiz 5
Main take-away

Ensemble of models

Module overview
Ensemble method using bootstrapping
Ensemble based on boosting
Hyperparameter tuning with ensemble methods
🏁 Wrap-up quiz 6
Main take-away

Evaluating model performance

Module overview
Comparing a model with simple baselines
Choice of cross-validation
Nested cross-validation
- Nested cross-validation
- ✅ Quiz M7.03
Classification metrics
Regression metrics
🏁 Wrap-up quiz 7
Main take-away

Concluding remarks

🎥 Concluding remarks
Concluding remarks

Appendix

Glossary
Datasets description
Acknowledgement
Notebook timings
Table of contents

🚧 Feature selection

Module overview
Benefits of using feature selection
Caveats of feature selection
Main take-away
✅ Quiz

🚧 Interpretation

Feature importance
✅ Quiz

Repository
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🎥 Visualizing scikit-learn pipelines in Jupyter

🎥 Visualizing scikit-learn pipelines in Jupyter#

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📃 Solution for Exercise M1.05

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