sklearn_ml_course

Machine Learning in Python with scikit-learn by France Université Numérique

This course is an in-depth introduction to predictive modeling with scikit-learn. Step-by-step and didactic lessons introduce the fundamental methodological and software tools of machine learning, and is as such a stepping stone to more advanced challenges in artificial intelligence, text mining, or data science. The course official page

Course Plan

• Module 1. The predictive modeling pipeline [lectures | solutions]
  • Tabular data exploration
  • Fitting scikit-learn model on numerical data
  • Handling categorical data
• Module 2. Selecting the best model [lectures | solutions]
  • Overfitting and underfitting
  • Validation and learning curves
  • Bias versus variance trade-off
• Module 3. Hyperparameters tuning [lectures | solutions]
  • Manual tuning
  • Automated tuning
• Module 4. Linear models [lectures | solutions]
  • Intuitions on linear models
  • Linear regressions
  • Modelling with a non-linear relationship data-target
  • Regularization in linear model
  • Linear model for classification
• Module 5. Decision tree models [lectures | solutions]
  • Intuitions on tree-based models
  • Decision tree in classification
  • Decision tree in regression
  • Hyperparameters of decision tree
• Module 6. Ensemble of models [lectures | solutions]
  • Ensemble method using bootstrapping
  • Ensemble based on boosting
  • Hyperparameters tuning with ensemble methods
• Module 7. Evaluating model performance [lectures | solutions]
  • Comparing a model with simple baselines
  • Choice of cross-validation
  • Nested cross-validation
  • Classification metrics
  • Regression metrics

The repository includes both notebooks from lectures and notebooks with my solutions to the given exercises and tests.