# kuleshov/cornell-cs5785-2020-applied-ml

Teaching materials for the applied machine learning course at Cornell Tech (online edition)

Repository: https://github.com/kuleshov/cornell-cs5785-2020-applied-ml
Canonical: https://ross.abutalabs.com/products/cornell-cs5785-2020-applied-ml
Language: Jupyter Notebook
License Family: other
Last push: 2022-10-12T13:41:55+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": 1986, "days_push": 1421, "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 1181, forks 288 (observed 2026-08-28T04:03:54.068489+00:00)

## What it is
Executable Jupyter notebook course notes and slides for Cornell Tech's Applied Machine Learning (CS5785) Fall 2020 course, accompanied by YouTube lecture videos. It is a free educational resource covering practical machine learning concepts.

## Use cases
- learn applied machine learning from a university course
- find lecture notes and slides for ML coursework
- run executable ML notebooks to study concepts
- self-study machine learning with video lectures
- use course materials for teaching an ML class
- review supervised and unsupervised learning examples

## When to choose
- you want structured, university-quality ML course materials for free
- you prefer learning via runnable Jupyter notebooks paired with video lectures
- you need teaching materials for an applied ML course

## When to avoid
- you need a production ML library or tool rather than educational content
- you require actively updated materials reflecting the latest ML research
- you need licensed or supported software for commercial use

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science
- domain: machine-learning, education, tutorials
- platform: python, cross-platform
- tags: course-materials, jupyter-notebooks, cornell-tech, lecture-notes, applied-ml

## Member repositories
- kuleshov/cornell-cs5785-2020-applied-ml (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:54.068489+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 2026-08-30T06:25:11.975760+00:00, confidence not recorded.
  - readme: https://github.com/kuleshov/cornell-cs5785-2020-applied-ml (fetched 2026-08-28T04:03:54.068489+00:00, sha 4776e257885c)
- Data as of 2026-08-30T08:39:29.467469+00:00.