Give elements on machine learning to participants of the SAGEX workshop (2019-07-30).
Before viewing the notebook an introduction using scaling law in physics will help set the scenery to understand the value of machine learning solutions.
A few tutorials can be found in notebooks/, they explain basic notion in python.
Notebooks on the classic iris and titanic as well as an analysis to the physical properties of water will be used to make first step on the subject.
Simple analysis of the PLAsTiCC Astronomical Classification dataset
https://www.kaggle.com/c/PLAsTiCC-2018
This code will not to win the competition (already over).
The aim is to show different data science analysis.
You should have python3, pip3 and jupyter notebook install locally on your machine.
The other packages will be installed on a virtual environment to avoid conflict in the packages.
Be very carefull when uninstalling python on a linux machine. The linux kernel uses python. Uninstalling python can render you machine unusable.
- Run:
python3 -m venv venv3_plasticc
- Activate virtual environment:
source venv3_plasticc/bin/activate
- Install packages:
pip3 install -r requirements.txt
- Add kernel to jupyter notebook:
python3 -m ipykernel install --user --name venv3_plasticc --display-name "plasticc"
- git: you can git clone the repo (see 5. to install)
git clone [email protected]:alexandreCameron/sagex.git
- zip: if you are not familiar with git you can download a zip archive
- Virtual environment: https://ipython.readthedocs.io/en/stable/install/kernel_install.html
- Python 3.6: https://www.python.org/downloads/release/python-368/
- Jupyter notebook: https://jupyter.org/install
- Git: https://git-scm.com/
- Git install tutorial: https://www.atlassian.com/git/tutorials/install-git