This repository showcases my learning journey in the world of DS and ML. It is also meant to publicly share comprehensive and reproducible examples written in Python that are supported by digested theory collected through hundreds of different sources. Currently here you can find detailed information on:
- classic ML (supervised and unsupervised)
- dl (different DL architectures in PyTorch)
- ds (A/B testing: frequentist and bayesian approaches)
- linalg (linear algebra concepts: svd, pca)
Tech stack: pytorch, torchvision, polars, wandb, scikit-learn, numpy, scipy, plotly