- data: contains datasets, additional files
- notebooks: contains Jupyter notebooks with solutions
- code: contains raw Python code format of the notebooks
- Exploratory data analysis (EDA)
- Unsupervised learning, clustering (PCA, tSNE)
- Supervised learning, KNN classification
- Linear regression
- Linear models for classification (Logistic regression)
- Model selection, regularization (Cross-validation, Lasso, Ridge, Elastic Net)
- SVM
- Decision trees, Random forest
- Neural networks (NN)
- Convolutional neural networks (CNN)
- More neural networks (pretrained models, transfer learning)