# h2oai/h2o-tutorials

Tutorials and training material for the H2O Machine Learning Platform

Repository: https://github.com/h2oai/h2o-tutorials
Canonical: https://ross.abutalabs.com/products/h2o-tutorials
Homepage: http://h2o.ai
Language: Jupyter Notebook
License Family: other
Topics: h2o, tutorial, machine-learning, data-science, deep-learning, python, r
Archived: true
Last push: 2024-10-24T17:25:46+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4046, "days_push": 678, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1500, forks 984 (observed 2026-08-28T04:04:54.168524+00:00)

## What it is
A collection of tutorials and training materials for the H2O-3 open-source machine learning platform, provided as Jupyter notebooks and R scripts. It covers topics like getting started with H2O, grid search and model selection, deep learning, stacked ensembles, and AutoML in both Python and R.

## Use cases
- learn h2o machine learning platform
- h2o automl tutorial
- grid search and model selection with h2o
- stacked ensembles example code
- h2o deep learning in r or python
- training material for h2o-3

## When to choose
- You are learning or teaching the H2O-3 platform and want runnable example notebooks
- You need reference code for AutoML, grid search, or stacked ensembles in H2O
- You want event-versioned tutorial snapshots matched to specific H2O releases

## When to avoid
- You need a production ML library rather than learning material - use H2O-3 itself
- You want tutorials for Driverless AI, LLM Studio, or other commercial H2O products
- You need a permissively licensed codebase - this repo has no license file

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, deep-learning
- domain: machine-learning, data-science, tutorials, education
- platform: python, jvm, cross-platform
- tags: h2o, jupyter-notebooks, automl, r-language, training-material, tutorial

## Member repositories
- h2oai/h2o-tutorials (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:54.168524+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 2026-08-30T04:32:59.922886+00:00, confidence not recorded.
  - readme: https://github.com/h2oai/h2o-tutorials (fetched 2026-08-28T04:04:54.168524+00:00, sha 5c6ec9e47fb1)
  - homepage: http://h2o.ai (fetched 2026-08-29T11:38:16.601745+00:00, sha 6b9a5ca9fced)
  - site_page: https://h2o.ai/docs (fetched 2026-08-29T11:38:16.623820+00:00, sha 411722578bc1)
  - site_page: https://h2o.ai/platform/enterprise-h2ogpte (fetched 2026-08-29T11:38:16.611024+00:00, sha 71f7b1d062f7)
  - site_page: https://h2o.ai/platform/why-h2o (fetched 2026-08-29T11:38:16.613654+00:00, sha dbfd58bb2a72)
  - site_page: https://h2o.ai/company (fetched 2026-08-29T11:38:16.615536+00:00, sha ddd0af08be22)
  - site_page: https://h2o.ai/company/press-media?tagFilter=Press+Release (fetched 2026-08-29T11:38:16.617327+00:00, sha 6aa60f9bcf14)
  - site_page: https://h2o.ai/partner-network/find-a-partner (fetched 2026-08-29T11:38:16.620159+00:00, sha 9d1a4050938e)
- Data as of 2026-08-30T08:39:29.467469+00:00.