# 100 Days of ML Code

100 Days of ML Coding

Repository: https://github.com/Avik-Jain/100-Days-Of-ML-Code
Canonical: https://ross.abutalabs.com/products/100-days-of-ml-code
License: MIT
License Family: permissive
Topics: machine-learning, machine-learning-algorithms, infographics, tutorial, siraj-raval-challenge, siraj-raval, implementation, 100-days-of-code-log, 100daysofcode, python, logistic-regression, naive-bayes-classifier, linear-regression, svm, linear-algebra, scikit-learn, support-vector-machines, deep-learning
Last push: 2023-12-29T07:57:53+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2981, "days_push": 978, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 51677, forks 11604 (observed 2026-08-28T04:12:11.909004+00:00)

## What it is
A curated 100-day machine learning curriculum with daily code walkthroughs and infographics covering algorithms like linear regression, logistic regression, KNN, and SVM. It includes datasets and a Chinese translation, popularized by the Siraj Raval challenge.

## Use cases
- learn machine learning from scratch in 100 days
- find beginner ML tutorials with infographics
- study implementations of logistic regression and SVM in Python
- follow a structured daily ML coding challenge
- get datasets and code examples for classic ML algorithms

## When to choose
- you want a structured, day-by-day path into machine learning
- you prefer visual infographics paired with hands-on Python code
- you are a beginner covering regression, classification, and scikit-learn basics

## When to avoid
- you need production-grade ML libraries or tooling
- you want up-to-date coverage of deep learning or LLMs
- you need actively maintained content with recent updates

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, deep-learning, tutorials, education
- platform: python
- tags: 100daysofcode, infographics, tutorial-series, scikit-learn, self-study

## Member repositories
- Avik-Jain/100-Days-Of-ML-Code (main) score 32
- Avik-Jain/100-Days-of-ML-Code-Chinese-Version (mirror) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:11.909004+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 2026-08-29T16:19:41.805657+00:00, confidence not recorded.
  - readme: https://github.com/Avik-Jain/100-Days-Of-ML-Code (fetched 2026-08-28T04:12:11.909004+00:00, sha 94e5cad6cbe1)
- Data as of 2026-08-30T08:39:29.467469+00:00.