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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