# aws-samples/aws-machine-learning-university-accelerated-tab

Machine Learning University: Accelerated Tabular Data Class

Repository: https://github.com/aws-samples/aws-machine-learning-university-accelerated-tab
Canonical: https://ross.abutalabs.com/products/aws-machine-learning-university-accelerated-tab
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: machine-learning, tabular-data, deep-learning, python, gluon, mxnet, sklearn
Last push: 2024-10-12T23:50:30+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2225, "days_push": 690, "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 1034, forks 300 (observed 2026-08-28T04:03:18.874016+00:00)

## What it is
An open course from AWS Machine Learning University covering machine learning techniques for tabular (spreadsheet-like) data, with slides, Jupyter notebooks, datasets, and YouTube lecture videos. It includes three lectures and a final project covering topics like KNN, tree-based models, feature engineering, and hyperparameter tuning.

## Use cases
- learn machine learning for tabular data
- find a free ML course with notebooks and videos
- study tree-based models and hyperparameter tuning
- get hands-on ML labs in Jupyter notebooks
- learn feature engineering and model evaluation
- start a career in machine learning with structured classes

## When to choose
- you want a free, structured introduction to ML on tabular data with runnable notebooks
- you prefer learning from video lectures paired with hands-on labs
- you want course material covering classic tabular ML techniques like KNN, bagging, and tree-based models

## When to avoid
- you need deep learning for images, text, or audio rather than tabular data
- you want production-grade ML tooling rather than educational material
- you need a maintained software library - this is course content, not a package

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, data-science
- domain: machine-learning, data-science, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, tabular-data, course-material, aws, scikit-learn, mxnet-gluon, video-lectures

## Member repositories
- aws-samples/aws-machine-learning-university-accelerated-tab (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:18.874016+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 2026-08-30T07:06:31.588015+00:00, confidence not recorded.
  - readme: https://github.com/aws-samples/aws-machine-learning-university-accelerated-tab (fetched 2026-08-28T04:03:18.874016+00:00, sha 23ddd95f14e1)
- Data as of 2026-08-30T08:39:29.467469+00:00.