Table of contents | 41026 | FUN-MOOC

Passer au contenu de cette vue

Welcome

Sequences	Units
Course presentation	🎬 Welcome to the Scikit-learn MOOC! Course presentation Team presentation Terms of use Forum
Evaluation & certification	Evaluation & certification Forum
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 🎬 Use of Jupyter notebooks and shortcuts Sandbox notebook
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	🎬 Introducing machine-learning concepts Quiz Intro.01

Module 1. The Predictive Modeling Pipeline

Sequences	Units
Module overview	Module overview
Tabular data exploration	First look at our dataset Exercise M1.01 Quiz M1.01
Fitting a scikit-learn model on numerical data	First model with scikit-learn Exercise M1.02 Working with numerical data 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 Using numerical and categorical variables together Exercise M1.05 🎬 Visualizing scikit-learn pipelines in Jupyter Quiz M1.03
Wrap-up quiz	Wrap-up quiz 1
Main take-away	Main take-away

Module 2. Selecting the best model

Sequences	Units
Module overview	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 Quiz M2.02
Bias versus variance trade-off	🎬 Bias versus variance Quiz M2.03
Wrap-up quiz	Wrap-up quiz 2
Main Take-away	Main Take away

Module 3. Hyperparameter tuning

Sequences	Units
Module overview	Module overview
Manual tuning	Set and get hyperparameters in scikit-learn Exercise M3.01 Quiz M3.01
Automated tuning	Hyperparameter tuning by grid-search Hyperparameter tuning by randomized-search 🎬 Analysis of hyperparameter search results Interactive exploration of hyper-parameter search results Evaluation and hyperparameter tuning Exercise M3.02 Quiz M3.02
Wrap-up quiz	Wrap-up quiz 3
Main take-away	Main take-away

Module 4. Linear Models

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

Module 5. Decision tree models

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

Module 6. Ensemble of models

Sequences	Units
Module overview	Module overview
Ensemble method using bootstrapping	🎬 Intuitions on ensemble models: bagging Introductory example to ensemble models Bagging Exercise M6.01 Random forest 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 Speeding-up gradient-boosting Quiz M6.02
Hyperparameter tuning with ensemble methods	Hyperparameter tuning Exercise M6.04 Quiz M6.03
Wrap-up quiz	Wrap-up quiz 6
Main take-away	Main take-away

Module 7. Evaluating model performance

Sequences	Units
Module overview	Module overview
Comparing a model with simple baselines	Comparing model performance with a simple baseline 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 Quiz M7.04
Regression metrics	Regression Exercise M7.03 Quiz M7.05
Wrap-up quiz	Wrap-up quiz 7
Main take-away	Main take-away

Conclusion

Sequences	Units
Concluding remarks	🎬 Concluding remarks Big messages and going further
Your general feedback	Share your feedback on the forum Survey: your experience and suggestions

Appendix

Sequences	Units
Datasets description	The penguins datasets The adult census dataset The California housing dataset Ames housing dataset The blood transfusion dataset The bike rides dataset
Acknowledgement	Figure attributions