Scikit-learn

 

• • Machine Learning concepts
• • Tabular data exploration
  • Fitting a scikit-learn model on numerical data
  • Handling categorical data
• • Overfitting and Underfitting
  • Validation and learning curves
  • Bias versus variance trade-off
• • Manual tuning
  • Automated tuning
• • Intuitions on linear models
  • Linear regression
  • Modelling with a non-linear relationship data-target
  • Regularization in linear model
  • Linear model for classification
• • Intuitions on tree-based models
  • Decisison tree in classification
  • Decision tree in regression
  • Hyperparameters of decision tree
• • Ensemble method using bootstrapping
  • Ensemble based on boosting
  • Hyperparameters tuning with ensemble methods
• • Comparing a model with simple baselines
  • Choice of cross-validation
  • Nested cross-validation
  • Classification metrics
  • Regression metrics