An introduction to Machine Learning (through notebook with the scikit ecocystem).
Create an environment for this tutorial:
conda create --name ml101 python=3.8
Switch to the branch (conda activate ml101) and install the required packages:
conda install numpy scipy scikit-learn matplotlib ipykernel jupyter conda install -c conda-forge py-xgboost
Make the environment available for the notebooks
python -m ipykernel install --user --name=ml101
Launch Jupyter to run the notebooks:
jupyter notebook
- Machine Learning paradigms (01-ml-paradigms)
- Regression (02-regression)
- Classification (03-classification)
- Complexity
- Overfitting (04-overfitting)
- Error decompositions (05-errors)
- Regularization (06-regularization)
- Model selection (07-model-selection)
- Advanced methods
- Ensemble (08-ensemble)
- Boosting (09-boosting)
- Deep learning (10-neural-nets)
- Appendix
- Useful tools (a1-tools)