# Yorko/mlcourse.ai

Open Machine Learning Course

Repository: https://github.com/Yorko/mlcourse.ai
Canonical: https://ross.abutalabs.com/products/mlcourseai
Homepage: https://mlcourse.ai
Language: Python
License: NOASSERTION
License Family: other
Topics: machine-learning, data-analysis, data-science, pandas, algorithms, numpy, scipy, matplotlib, seaborn, plotly, scikit-learn, kaggle-inclass, vowpal-wabbit, python, ipynb, docker, math
Last push: 2026-03-01T09:12:51+00:00

## Health v2 (maintenance only)
Score: 54/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 70, release rhythm 8, longevity 100
- inputs: {"age_days": 3474, "days_push": 185, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10693, forks 5696 (observed 2026-08-28T04:10:43.791751+00:00)

## What it is
mlcourse.ai is an open, self-paced machine learning course by OpenDataScience, combining math-heavy lectures with practical assignments and Kaggle Inclass competitions. The repository hosts Jupyter notebook materials covering Python data analysis and core ML algorithms.

## Use cases
- learn machine learning from scratch
- self-paced ML course with assignments
- practice data analysis with pandas and scikit-learn
- prepare for Kaggle competitions
- study ML theory with math and practical exercises
- find Jupyter notebook tutorials on machine learning

## When to choose
- you want a free, structured ML course balancing theory and practice
- you learn best through hands-on notebooks and competitions
- you want to strengthen pandas, numpy, and scikit-learn skills

## When to avoid
- you need a software library or tool rather than course material
- you want deep learning or LLM-focused content
- you need solutions to all assignments without a paid bonus pack

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science, data-visualization
- domain: machine-learning, data-science, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, kaggle, self-paced-course, scikit-learn, pandas, open-course

## Member repositories
- Yorko/mlcourse.ai (main) score 54

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:43.791751+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 2026-08-29T17:17:58.424676+00:00, confidence not recorded.
  - readme: https://github.com/Yorko/mlcourse.ai (fetched 2026-08-28T04:10:43.791751+00:00, sha f9c147863434)
  - homepage: https://mlcourse.ai (fetched 2026-08-29T08:17:07.755877+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.