A list of useful blog posts, papers, and websites for all things data science
- Technical Papers with code: https://paperswithcode.com/sota?fbclid=IwAR0e4hxCX0FFEpIvnGAp1scgBLSn84pB7DHvdTlQW4bSkLleQatZMec8nx0
- Setting up data science repo: https://drivendata.github.io/cookiecutter-data-science/
- Linux intro: https://www.guru99.com/linux-differences.html
- Mac Terminal Setup: https://medium.freecodecamp.org/jazz-up-your-zsh-terminal-in-seven-steps-a-visual-guide-e81a8fd59a38
- virtualenv: https://www.dabapps.com/blog/introduction-to-pip-and-virtualenv-python/
- Docker: https://rominirani.com/docker-tutorial-series-a7e6ff90a023
- DS Interview Questions: https://github.com/alexeygrigorev/data-science-interviews
- Applications of ML: https://github.com/eugeneyan/applied-ml
- Good overview of how python virtual environments work: https://towardsdatascience.com/virtual-environments-104c62d48c54
- A Practical Guide to Using setup.py: https://godatadriven.com/blog/a-practical-guide-to-using-setup-py/
- How to Start a Data Science Project in Python: https://godatadriven.com/blog/how-to-start-a-data-science-project-in-python/
- Organize Python code like a PRO: https://guicommits.com/organize-python-code-like-a-pro/
- Relieving your Python packaging pain: https://www.bitecode.dev/p/relieving-your-python-packaging-pain
- Correlation and Statistical Inference: https://towardsdatascience.com/eveything-you-need-to-know-about-interpreting-correlations-2c485841c0b8
- Awesome Notebook by Victor Dibia: https://colab.research.google.com/drive/1pjPzsw_uZew-Zcz646JTkRDhF2GkPk0N?usp=sharing#scrollTo=yZQ3I4GuPwvw
- https://christophm.github.io/interpretable-ml-book/
- Interpretability examples: https://github.com/jphall663/interpretable_machine_learning_with_python
- Deep Learning Book with Notebooks: http://d2l.ai/
- Seq2seq and Attention: https://jalammar.github.io/visualizing-neural-machine-translation-mechanics-of-seq2seq-models-with-attention/
- Transformers: https://jalammar.github.io/illustrated-transformer/
- BERT: https://jalammar.github.io/illustrated-bert/
- RNN's: https://machinelearningmastery.com/models-sequence-prediction-recurrent-neural-networks/
- Feature Selection: https://www.datacamp.com/community/tutorials/feature-selection-python
- Choosing a feature selection method: https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/
- Model Evaluation: https://www.ritchieng.com/machine-learning-evaluate-classification-model/
- Best Practices: https://12factor.net/
- Building a python package: https://antonz.org/python-packaging/
- The Good Research Code Handbook: https://goodresearch.dev/index.html
- How to Set Up a Python Project For Automation and Collaboration: https://eugeneyan.com/writing/setting-up-python-project-for-automation-and-collaboration/
- Ultimate Guide to Deploying ML Models: https://mlinproduction.com/deploying-machine-learning-models/