There are two different ways to approch the machine learning (ML) exercises of this course:
1) If you want to work directly on-line you have to work on the "notebooks" following these instructions:
- Go on google colab and log with your google account (https://colab.research.google.com/)
- Open the Github option and set the connection following what is written in the following picture :
Below there are instructions for the standard install of Python > 3.7 for the course.
- Download the appropriate version of miniconda for your computing environment: https://conda.io/miniconda.html
- Follow the "quick install" instructions to install miniconda on your machine: https://conda.io/docs/install/quick.html
- Once you have installed miniconda, we need to set up a conda environment for our work. (To learn more about conda virtual environments, consult their docs: https://conda.io/docs/using/envs.html) From the command line (note this is slightly different on a Windows machine), enter the following: conda env create -f environment.yml
- To activate the environment, this should be done prior to any work at the course, enter the following on the command line: conda activate OAR_ML_env
- To exit the environment, type the following: conda deactivate
If you don't want to install Anaconda or python on your pc, you still can work on the notebooks:
- go on google colab and log with your google account
- open the Github option and set the connection with this repository (see the picture) :
If you are very new with python, please read these tutorials before the course:
- https://jckantor.github.io/CBE30338/01.01-Getting-Started-with-Python-and-Jupyter-Notebooks.html
- https://github.com/milaan9/01_Python_Introduction
All the notebooks we will use are coming from:
- https://github.com/LSSTC-DSFP/LSSTC-DSFP-Sessions
- https://github.com/JWarmenhoven/Coursera-Machine-Learning/tree/master/notebooks
For any questions or problems don't hesistate to contact me at [email protected]