ML FUN
ML-FUN
Course information:
Course website
https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/
Course github repo with notebooks
https://github.com/INRIA/scikit-learn-mooc/
Course as static website
https://inria.github.io/scikit-learn-mooc/
Course forums (direct link)
https://mooc-forums.inria.fr/moocsl/
Resources
Where to run the notebooks
- online at the course website
- https://mybinder.org
- locally (via conda) - https://github.com/INRIA/scikit-learn-mooc/blob/master/local-install-instructions.md
- locally (via docker)
LOCAL_DIRECTORY=~/Downloads/jupyter/mountdir
mkdir -p $LOCAL_DIRECTORY
docker run -p 8888:8888 -v $LOCAL_DIRECTORY:/home/jovyan/ jupyter/scipy-notebook
Datasets
https://github.com/INRIA/scikit-learn-mooc/tree/master/datasets
Various ML resources
- One hot encoding for categorical data: https://machinelearningmastery.com/one-hot-encoding-for-categorical-data/
- Scikit learn introduction: https://www.tutorialspoint.com/scikit_learn/scikit_learn_introduction.htm
- https://scikit-learn.org/stable/index.html
- https://pandas.pydata.org/docs/pandas.pdf
- This official 10 min intro to Pandas: https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html
- Pandas cheat sheet https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf
- https://www.kaggle.com/learn
- Intro to Jupyter Notebooks https://github.com/fastai/fastbook/blob/master/app_jupyter.ipynb
- Vim extension for Jupyter Notebooks https://github.com/lambdalisue/jupyter-vim-binding
- Built-in magic commands https://ipython.readthedocs.io/en/stable/interactive/magics.html
Apply the knowledge that you’ve learned on projects or Kaggle competitions
Meetup events
past
- Module 1 - 27.05.2021
- Module 2 - 01.06.2021
- Module 3 - 10.06.2021
- Module 4 - 17.06.2021
- Module 5 - 24.06.2021
- Module 6 - 01.07.2021
- Module 7 - 08.06.2021
Fun stuff
Imports to try ;)
import antigravity
import this
import from __future__ import braces
