Machine learning in Python with scikit-learn

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
Intuitions on ensemble of tree-based 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
Introduction of the evaluation metrics
Classification metrics
Regression metrics