ML-2021

Repository for the ML (in Italian: "AAUT - Apprendimento AUTomatico") exam's exercises, session 2020-2021.

This repisotory is structured in 3 folders:

• data: containing the datasets for the exercises.
• notebooks: containing the Jupyter notebooks for the exercises. Each notebook is located in a folder with the name of the topic (for example: "tree-models", "linear-models").
• scripts: containing the notebook converted in python scripts.

Moreover, you can also find in fhe enviroment.yml file, my conda enviroment configuration.

Further readings: best practise for using Jupyter Notebook repository

1. Naming convention: This is really going to depend on your personal preferences, and use cases. Here is the approach I use [#]_[2-4 word description]_[author-initials]_[ISO 8601 date].ipynb for example: 1_exploratory_analysis_ag_2019-02-16.ipynb
2. How to organize your Python data science project
3. Jupyter Notebook Best Practise
4. How to Organize Your Data Science Project