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Scikit-learn course - Home
  • 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
    • 🎥 Validation of a model
    • Model evaluation using cross-validation
    • ✅ 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
    • 🎥 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
    • 🎥 Overfitting and Underfitting
    • Cross-validation framework
    • ✅ 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 2
  • 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
    • 🎥 Analysis of hyperparameter search results
    • Analysis of hyperparameter search results
    • Evaluation and hyperparameter tuning
    • 📝 Exercise M3.02
    • 📃 Solution for Exercise M3.02
    • ✅ Quiz M3.02
  • 🏁 Wrap-up quiz 3
  • Main take-away

Linear models

  • Module overview
  • Intuitions on linear models
    • 🎥 Intuitions on linear models
    • Linear regression without scikit-learn
    • 📝 Exercise M4.01
    • 📃 Solution for Exercise M4.01
    • Linear regression using scikit-learn
    • Linear models for classification
    • ✅ Quiz M4.01
  • Non-linear feature engineering for linear models
    • Non-linear feature engineering for Linear Regression
    • 📝 Exercise M4.02
    • 📃 Solution for Exercise M4.02
    • Non-linear feature engineering for Logistic Regression
    • 📝 Exercise M4.03
    • 📃 Solution for Exercise M4.03
    • ✅ Quiz M4.02
  • Regularization in linear model
    • 🎥 Intuitions on regularized linear models
    • Regularization of linear regression model
    • 📝 Exercise M4.04
    • 📃 Solution for Exercise M4.04
    • ✅ Quiz M4.03
  • 🏁 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
    • 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 5
  • Main take-away

Ensemble of models

  • Module overview
  • Ensemble method using bootstrapping
    • 🎥 Intuitions on ensemble models: bagging
    • Introductory example to ensemble models
    • Bagging
    • 📝 Exercise M6.01
    • 📃 Solution for Exercise M6.01
    • Random forests
    • 📝 Exercise M6.02
    • 📃 Solution for Exercise M6.02
    • ✅ Quiz M6.01
  • Ensemble based on boosting
    • 🎥 Intuitions on ensemble models: boosting
    • Adaptive Boosting (AdaBoost)
    • Gradient-boosting decision tree (GBDT)
    • 📝 Exercise M6.03
    • 📃 Solution for Exercise M6.03
    • Speeding-up gradient-boosting
    • ✅ Quiz M6.02
  • Hyperparameter tuning with ensemble methods
    • Hyperparameter tuning
    • 📝 Exercise M6.04
    • 📃 Solution for Exercise M6.04
    • ✅ Quiz M6.03
  • 🏁 Wrap-up quiz 6
  • Main take-away

Evaluating model performance

  • Module overview
  • Comparing a model with simple baselines
    • Comparing model performance with a simple baseline
    • 📝 Exercise M7.01
    • 📃 Solution for Exercise M7.01
    • ✅ Quiz M7.01
  • Choice of cross-validation
    • Stratification
    • Sample grouping
    • Non i.i.d. data
    • ✅ Quiz M7.02
  • Nested cross-validation
    • Nested cross-validation
    • ✅ Quiz M7.03
  • 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 7
  • Main take-away

Concluding remarks

  • 🎥 Concluding remarks
  • Concluding remarks

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

🚧 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
  • Repository
  • Suggest edit
  • Open issue
  • .md

Classification metrics

Classification metrics#

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

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✅ Quiz M7.03

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Classification

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