ML project structure in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main purpose of having a clear ML project structure?

A clear ML project structure helps organize code, data, and experiments so the project is easy to understand, reproduce, and maintain.

beginner

Name three common folders you might find in an ML project.

Common folders include data (for raw and processed data), src (source code), and models (saved trained models).

intermediate

Why should raw data be kept separate from processed data in an ML project?

Keeping raw data separate ensures the original data is never changed accidentally and allows easy reprocessing if needed.

beginner

What role does a README file play in an ML project?

A README file explains the project purpose, how to run the code, and any important details, helping others understand and use the project.

intermediate

How can version control help in managing an ML project?

Version control tracks changes in code and files, allowing you to go back to previous versions and collaborate safely with others.

Which folder typically contains the scripts to train and test ML models?

A data

B src

C models

D docs

The src folder usually holds source code like training and testing scripts.

Why is it important to separate raw data from processed data?

A To prevent accidental changes to original data

B To save disk space

C To speed up training

D To make code shorter

Separating raw data protects the original data from accidental edits.

What is the purpose of the 'models' folder in an ML project?

A Store raw data files

B Keep visualization images

C Write documentation

D Save trained model files

The models folder stores saved trained models for reuse or deployment.

Which file helps others understand how to use your ML project?

A README.md

B requirements.txt

C train.py

D data.csv

The README.md file explains project details and usage instructions.

How does version control benefit ML projects?

A Automatically improves model accuracy

B Stores large datasets

C Tracks changes and supports collaboration

D Runs code faster

Version control helps track code changes and allows multiple people to work together safely.

Describe the typical folder structure of an ML project and the purpose of each folder.

Think about where you keep data, code, and explanations.

Explain why organizing data and code properly is important in an ML project.

Consider what happens if files are mixed up or missing.