# codenihar/ml

This repo contains all the resources related to the massive mass coders machine learning series

Repository: https://github.com/codenihar/ml
Canonical: https://ross.abutalabs.com/products/codenihar-ml
Homepage: https://youtube.com/playlist?list=PL2Kd-KQLppEFPcA8abnNy5YKCxOoFdUUc&si=fBr5zusj7NcUvQL5
License Family: other
Last push: 2024-07-13T07:45:07+00:00

## Health v2 (maintenance only)
Score: 24/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 61
- inputs: {"age_days": 859, "days_push": 781, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2116, forks 61 (observed 2026-08-28T04:06:16.311214+00:00)

## What it is
A collection of Google Colab notebooks accompanying the Mass Coders Machine Learning YouTube series, taught in Telugu. It serves as a centralized hub of educational resources covering machine learning from introductory to advanced topics.

## Use cases
- learn machine learning basics through notebooks
- follow along with a machine learning video series
- find beginner-friendly ML Colab notebooks
- learn machine learning in Telugu
- practice ML concepts hands-on in Colab
- contribute educational ML notebooks

## When to choose
- you are a beginner wanting structured ML tutorials
- you prefer learning in Telugu
- you want runnable Colab notebooks to follow a video series

## When to avoid
- you need production-ready ML code or libraries
- you need English-only or formally maintained course material
- you need a software tool rather than learning resources

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning
- domain: machine-learning, tutorials, education
- platform: python
- tags: colab-notebooks, youtube-series, telugu-language, educational

## Member repositories
- codenihar/ml (main) score 24

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:16.311214+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:53:19.419987+00:00, confidence not recorded.
  - readme: https://github.com/codenihar/ml (fetched 2026-08-28T04:06:16.311214+00:00, sha c0d2c9243347)
  - homepage: https://youtube.com/playlist?list=PL2Kd-KQLppEFPcA8abnNy5YKCxOoFdUUc&si=fBr5zusj7NcUvQL5 (fetched 2026-08-29T10:33:41.041781+00:00, sha aef8f951978d)
- Data as of 2026-08-30T08:39:29.467469+00:00.