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Machine Learning Concepts

πŸŽ₯ Introducing machine-learning concepts

βœ… Quiz Intro.01

The predictive modeling pipeline

Module overview

Tabular data exploration

Fitting a scikit-learn model on numerical data

Handling categorical data

🏁 Wrap-up quiz 1

Main take-away

Selecting the best model

Module overview

Overfitting and underfitting

Validation and learning curves

Bias versus variance trade-off

🏁 Wrap-up quiz 2

Main take-away

Hyperparameter tuning

Module overview

Manual tuning

Automated tuning

🏁 Wrap-up quiz 3

Main take-away

Linear models

Module overview

Intuitions on linear models

Linear regression

Modelling non-linear features-target relationships

Regularization in linear model

Linear model for classification

🏁 Wrap-up quiz 4

Main take-away

Decision tree models

Module overview

Intuitions on tree-based models

Decision tree in classification

Decision tree in regression

Hyperparameters of decision tree

🏁 Wrap-up quiz 5

Main take-away

Ensemble of models

Module overview

Ensemble method using bootstrapping

Ensemble based on boosting

Hyperparameter tuning with ensemble methods

🏁 Wrap-up quiz 6

Main take-away

Evaluating model performance

Module overview

Comparing a model with simple baselines

Choice of cross-validation

Nested cross-validation

Classification metrics

Regression metrics

🏁 Wrap-up quiz 7

Main take-away

Concluding remarks

πŸŽ₯ Concluding remarks

Concluding remarks