full-index.md

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
• ✅ 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
• ✅ 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
• Regularization of linear regression model
• 📝 Exercise M4.04
• 📃 Solution for Exercise M4.04
• ✅ Quiz M4.04

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 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