HashSlap-Summer-of-Code/ml-core

Create a Comprehensive ML Tutorial Series

Open

#8 opened on Jun 18, 2025

View on GitHubContributor guide
 (2 comments) (0 reactions) (1 assignee)Jupyter Notebook (11 forks)auto 404
Intermediateenhancementhacktoberfesthssoc

Repository metrics

Stars
 (3 stars)
PR merge metrics
 (PR metrics pending)

Description

Description:
Many folders contain valuable code but lack beginner-friendly explanations. Add tutorial-style documentation in each folder to help users understand the theory and implementation of the algorithms.

Expected Tasks:

  • Add a README.md to subfolders like computer-vision, data-analysis, etc., containing:
    • A brief introduction to the domain.
    • A step-by-step walkthrough of a key implementation (e.g., how KNN works).
    • Sample input and expected output.
    • Hyperlink to helpful resources (e.g., scikit-learn, Coursera, blog posts).
  • Ensure the code runs correctly with the tutorial.
  • Add a table of contents or index in the root README.md to guide users to these tutorials.

Stretch Goal:

  • Add visual diagrams or flowcharts using Markdown-supported images or ASCII diagrams.

Contributor guide

Research direction
Check the folder structure and identify which subfolders lack README files. Then, write a tutorial style README for each, including a step by step walkthrough of a key implementation, sample input/output, and links to resources. Ensure existing code runs correctly. Optionally, add visual diagrams using ASCII or Markdown images.
Tech stack
python
Domain
machine learningdocumentation
Issue type
Documentation
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
GitPythonBasic ML concepts
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
85