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Scikit-learn course
  • 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
    • ✅ Quiz 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
    • ✅ Quiz M1.02
  • Handling categorical data
    • Encoding of categorical variables
    • 📝 Exercise M1.04
    • 📃 Solution for Exercise M1.04
    • Using numerical and categorical variables together
    • 📝 Exercise M1.05
    • 📃 Solution for Exercise M1.05
    • ✅ Quiz M1.03
  • 🏁 Wrap-up quiz
  • Main take-away

Selecting the best model

  • Module overview
  • Overfitting and underfitting
    • 📰 Overfitting and Underfitting
    • The framework and why do we need it
    • ✅ Quiz M2.01
  • Validation and learning curves
    • 📰 Comparing train and test errors
    • Overfit-generalization-underfit
    • Effect of the sample size in cross-validation
    • 📝 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
  • Main take-away

Hyperparameter tuning

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

Linear models

  • Module overview
  • Intuitions on linear models
    • 📰 Intuitions on linear models
    • ✅ Quiz M4.01
  • Linear regression
    • Linear regression without scikit-learn
    • 📝 Exercise M4.01
    • 📃 Solution for Exercise M4.01
    • Linear regression using scikit-learn
    • ✅ Quiz M4.02
  • Modelling non-linear features-target relationships
    • 📝 Exercise M4.02
    • 📃 Solution for Exercise M4.02
    • Linear regression for a non-linear features-target relationship
    • 📝 Exercise M4.03
    • 📃 Solution for Exercise M4.03
    • ✅ Quiz M4.03
  • Regularization in linear model
    • 📰 Intuitions on regularized linear models
    • ✅ Quiz M4.04a
    • Regularization of linear regression model
    • 📝 Exercise M4.04
    • 📃 Solution for Exercise M4.04
    • ✅ Quiz M4.04b
  • Linear model for classification
    • Linear model for classification
    • 📝 Exercise M4.05
    • 📃 Solution for Exercise M4.05
    • Beyond linear separation in classification
    • ✅ Quiz M4.05
  • 🏁 Wrap-up quiz
  • 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
    • Build a classification decision tree
    • 📝 Exercise M5.01
    • 📃 Solution for Exercise M5.01
    • ✅ Quiz M5.02
  • Decision tree in regression
    • Decision tree for regression
    • 📝 Exercise M5.02
    • 📃 Solution for Exercise M5.02
    • ✅ Quiz M5.03
  • Hyperparameters of decision tree
    • Importance of decision tree hyperparameters on generalization
    • ✅ Quiz M5.04
  • 🏁 Wrap-up quiz
  • Main take-away

Ensemble of models

  • Module overview
  • Intuitions on ensemble methods
    • 📰 Intuitions on ensemble of tree-based models
    • ✅ Quiz M6.01
    • Introductory example to ensemble models
  • Ensemble method using bootstrapping
    • Bagging
    • 📝 Exercise M6.01
    • 📃 Solution for Exercise M6.01
    • Random forests
    • 📝 Exercise M6.02
    • 📃 Solution for Exercise M6.02
    • ✅ Quiz M6.02
  • Ensemble based on boosting
    • Adaptive Boosting (AdaBoost)
    • 📝 Exercise M6.03
    • 📃 Solution for Exercise M6.03
    • Gradient-boosting decision tree (GBDT)
    • 📝 Exercise M6.04
    • 📃 Solution for Exercise M6.04
    • Speeding-up gradient-boosting
    • ✅ Quiz M6.03
  • Hyperparameter tuning with ensemble methods
    • Hyperparameter tuning
    • 📝 Exercise M6.05
    • 📃 Solution for Exercise M6.05
    • ✅ Quiz M6.04
  • 🏁 Wrap-up quiz
  • Main take-away

Evaluating model performance

  • Module overview
  • Comparing a model with simple baselines
    • Comparing results with baseline and chance level
    • 📝 Exercise M7.01
    • 📃 Solution for Exercise M7.01
    • ✅ Quiz M7.01
  • Choice of cross-validation
    • 📝 Introductory exercise regarding stratification
    • Stratification
    • 📝 Introductory exercise for sample grouping
    • Sample grouping
    • 📝 Introductory exercise for non i.i.d. data
    • Non i.i.d. data
    • ✅ Quiz M7.02
  • Nested cross-validation
    • Nested cross-validation
    • ✅ Quiz M7.03
  • Introduction of the evaluation metrics
  • Classification metrics
    • Classification
    • 📝 Exercise M7.02
    • 📃 Solution for Exercise M7.02
    • ✅ Quiz M7.04
  • Regression metrics
    • Regression
    • 📝 Exercise M7.03
    • 📃 Solution for Exercise M7.03
    • ✅ Quiz M7.05
  • 🏁 Wrap-up quiz
  • Main take-away

🚧 Feature selection

  • Module overview
  • Benefits of using feature selection
  • Caveats of feature selection
    • 📝 Exercise 01
    • 📃 Solution for Exercise 01
    • Limitation of selecting feature using a model
  • Main take-away
  • ✅ Quiz

🚧 Interpretation

  • Feature importance
  • ✅ Quiz

Appendix

  • Glossary
  • Datasets description
    • The penguins datasets
    • The adult census dataset
    • The California housing dataset
    • The Ames housing dataset
    • The blood transfusion dataset
    • The bike rides dataset
  • Acknowledgement
  • Notebook timings
  • Table of contents
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Classification metrics¶

  • Classification
  • 📝 Exercise M7.02
  • 📃 Solution for Exercise M7.02
  • ✅ Quiz M7.04

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Introduction of the evaluation metrics

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Classification

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