tgbdc7 / 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/

Resources

Where to run the notebooks

• online at 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

Meetup events

• https://www.meetup.com/hs3city/events/278213715/

Fun stuff

Imports to try ;)

import antigravity
import this
import from __future__ impor braces