demianw / ca_09_2020

Data Science Credit Agricole

Curriculum

This lecture is focused on the following concepts:

1. Introduction the Python programming language;
2. Data wrangling using Pandas;
3. Applied mathematics using NumPy;
4. Understand linear models;
5. Understand tree-based algorithms;
6. Manage mixed data types in machine-learning pipeline;
7. Fine tuning model by hyper-parameters search.

Additional material:

Some intro slides: http://ogrisel.github.io/decks/2017_intro_sklearn

Getting started

In case that you have any issues, you click on the binder link below which will setup an online machine for you:

Alternatively you can create a new conda environment which will be called dssp by default and whill contain all the packages required to run the notebooks:

conda env create -f environment.yml
conda activate dssp

cd path/to/ca_09_2020
jupyter notebook

You can also update an existing conda environment:

conda env update -f environment.yml

References

This material is inspired and reused part of the following materials:

• https://github.com/amueller/scipy-2018-sklearn
• https://github.com/lesteve/euroscipy-2019-scikit-learn-tutorial