Phyton

Accede como invitado para ver el curso.

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