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Course plan
Introduction
Machine Learning concepts
Module 1. The Predictive Modeling Pipeline
- Tabular data exploration
- Fitting a scikit-learn model on numerical data
- Handling categorical data
Module 2. Selecting the best model
- Overfitting and Underfitting
- Validation and learning curves
- Bias versus variance trade-off
Module 3. Hyperparameters tuning
- Manual tuning
- Automated tuning
Module 4. Linear Models
- Intuitions on linear models
- Linear regression
- Modelling with a non-linear relationship data-target
- Regularization in linear model
- Linear model for classification
Module 5. Decision tree models
- Intuitions on tree-based models
- Decisison tree in classification
- Decision tree in regression
- Hyperparameters of decision tree
Module 6. Ensemble of models
- Ensemble method using bootstrapping
- Ensemble based on boosting
- Hyperparameters tuning with ensemble methods
Module 7. Evaluating model performance
- Comparing a model with simple baselines
- Choice of cross-validation
- Nested cross-validation
- Classification metrics
- Regression metrics