Table of contents

• 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
• Take Away
• ✅ Quiz