Table of contents | 41026 | FUN-MOOC

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Welcome

Sequences	Units
Course presentation	Welcome to the Scikit-learn MOOC! Course presentation Team presentation Terms of use
Evaluation & certification	Evaluation & certification
How to use the FUN Platform?	How to use the FUN Platform? Forum: Your questions on FUN platform
Getting Started with Jupyter	Getting started with Jupyter Sandbox notebook Forum
How to use the Forum ?	Discussion forum guide General Terms of Use Privacy Policy Forum: Home page Forum: Introduce yourself
Start-of-course survey	Survey: profile and expectations

Introduction: Machine Learning concepts

Sequences	Units
Introducing machine-learning concepts	Video: What is Machine Learning ? Quiz Intro.01 Forum

Module 1. The Predictive Modeling Pipeline

Sequences	Units
Module overview	Module overview
Tabular data exploration	Lecture: First look at our dataset Exercise M1.01 Quiz M1.01 Forum
Fitting a scikit-learn model on numerical data	Lecture: First model with scikit-learn Exercise M1.02 Lecture: Working with numerical data Exercise M1.03 Lecture: Preprocessing for numerical features Quiz M1.02 Forum
Handling categorical data	Lecture: Encoding of categorical variables Exercise M1.04 Lecture: Using numerical and categorical variables together Exercise M1.05 Quiz M1.03 Forum
Wrap-up quiz	Wrap-up quiz 1 Forum
Main take-away	Main take-away

Module 2. Selecting the best model

Sequences	Units
Module overview	Module overview
Overfitting and Underfitting	Video Overfitting and Underfitting Lecture: The framework and why do we need it Quiz M2.01 Forum
Validation and learning curves	Video Comparing train and test errors Overfit-generalization-underfit Effect of the sample size in cross-validation Exercise M2.01 Quiz M2.02 Forum
Bias versus variance trade-off	Video Bias versus variance Quiz M2.03 Forum
Wrap-up quiz	Wrap-up quiz 2 Forum
Main Take-away	Main Take away

Module 3. Hyperparameter tuning

Sequences	Units
Module overview	Module overview
Manual tuning	Lecture: Set and get hyperparameters in scikit-learn Exercise M3.01 Quiz M3.01 Forum
Automated tuning	Hyperparameter tuning by grid-search Hyperparameter tuning by randomized-search Cross-validation and hyperparameter tuning Exercise M3.02 Quiz M3.02 Forum
Wrap-up quiz	Wrap-up quiz 3 Forum
Main take-away	Main take-away

Module 4. Linear Models

Sequences	Units
Module overview	Module overview
Intuitions on linear models	Video Intuitions on linear models Quiz M4.01 Forum
Linear regression	Lecture: Linear regression without scikit-learn Exercise M4.01 Lecture: Linear regression using scikit-learn Quiz M4.02 Forum
Modelling non-linear features-target relationships	Exercise M4.02 Lecture: Linear regression with non-linear link between data and target Exercise M4.03 Quiz M4.03 Forum
Regularization in linear model	Video Regularized linear models Quiz M4.04a Lecture: Regularization of linear regression model Exercise M4.04 Quiz M4.04b Forum
Linear model for classification	Lecture: Linear model for classification Exercise M4.05 Lecture: Beyond linear separation in classification Quiz M4.05 Forum
Wrap-up quiz	Wrap-up quiz 4 Forum
Main take-away	Main take-away

Module 5. Decision tree models

Sequences	Units
Module overview	Module overview
Intuitions on tree-based models	Video Intuitions on tree-based models Quiz M5.01 Forum
Decisison tree in classification	Lecture: Build a classification decision tree Exercise M5.01 Quiz M5.02 Forum
Decision tree in regression	Lecture: Decision tree for regression Exercise M5.02 Quiz M5.03 Forum
Hyperparameters of decision tree	Lecture: Importance of decision tree hyperparameters on generalization Quiz M5.04 Forum
Wrap-up quiz	Wrap-up quiz 5 Forum
Main take-away	Main take-away

Module 6. Ensemble of models

Sequences	Units
Module overview	Module overview
Intuitions on ensemble methods	Video Intuitions on ensemble of tree-based models Quiz M6.01 Introductory example to ensemble models Forum
Ensemble method using bootstrapping	Lecture: Bagging Exercise M6.01 Lecture: Random forest Exercise M6.02 Quiz M6.02 Forum
Ensemble based on boosting	Lecture: Adaptive Boosting (AdaBoost) Exercise M6.03 Lecture: Gradient-boosting decision tree (GBDT) Exercise M6.04 Lecture: Speeding-up gradient-boosting Quiz M6.03 Forum
Hyperparameter tuning with ensemble methods	Lecture: Hyperparameter tuning Exercise M6.05 Quiz M6.04 Forum
Wrap-up quiz	Wrap-up quiz 6 Forum
Main take-away	Main take-away

Module 7. Evaluating model performance

Sequences	Units
Module overview	Module overview
Comparing a model with simple baselines	Lecture: Comparing results with baseline and chance level Exercise M7.01 Quiz M7.01 Forum
Choice of cross-validation	Lecture: Introductory exercise regarding stratification Stratification Lecture: Stratification Lecture: Introductory exercise for sample grouping Sample grouping Lecture: Sample grouping Exercise Introductory exercise for non i.i.d. data Lecture: Non i.i.d. data Quiz M7.02 Forum
Nested cross-validation	Lecture: Nested cross-validation Quiz M7.03 Forum
Introduction of the evaluation metrics	Introduction of the evaluation metrics
Classification metrics	Lecture: Classification Exercise M7.02 Quiz M7.04 Forum
Regression metrics	Lecture: Regression Exercise M7.03 Quiz M7.05 Forum
Wrap-up quiz	Wrap-up quiz 7 Forum
Main take-away	Main take-away

Appendix

Sequences	Units
Datasets description	Lecture: The penguins datasets The adult census dataset Lecture: The California housing dataset Lecture: The Ames housing dataset Lecture: The blood transfusion dataset Forum
Acknowledgement	Figure attributions

End of course

Sequences	Units
Survey: your experience and suggestions	Survey: your experience and suggestions