# mrdbourke/zero-to-mastery-ml

All course materials for the Zero to Mastery Machine Learning and Data Science course.

Repository: https://github.com/mrdbourke/zero-to-mastery-ml
Canonical: https://ross.abutalabs.com/products/zero-to-mastery-ml
Homepage: https://dbourke.link/ZTMmlcourse
Language: Jupyter Notebook
License Family: other
Topics: machine-learning, data-science, deep-learning
Last push: 2024-10-30T03:31:14+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": 2536, "days_push": 672, "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 6887, forks 4206 (observed 2026-08-28T04:09:51.119357+00:00)

## What it is
A repository of all course materials (Jupyter notebooks, code, datasets, images) for the Zero to Mastery Machine Learning and Data Science course by Daniel Bourke. It covers NumPy, pandas, Matplotlib, scikit-learn, and TensorFlow deep learning through a structured, project-based curriculum also available as an online book.

## Use cases
- learn machine learning from scratch
- learn data science with python
- find beginner machine learning tutorials with jupyter notebooks
- learn numpy pandas and matplotlib for data analysis
- study scikit-learn and tensorflow with example projects
- get free course materials for a machine learning bootcamp
- practice end-to-end ml projects like price regression

## When to choose
- you are a complete beginner wanting a structured, project-based path into machine learning and data science
- you prefer learning through runnable Jupyter notebooks with included datasets
- you want free access to materials that pair with a popular video course or online book

## When to avoid
- you need a production-ready library or tool rather than educational content
- you are looking for advanced or research-level machine learning material
- you need software with an explicit license for redistribution

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, data-science, data-visualization, deep-learning
- domain: machine-learning, data-science, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, course-materials, numpy, pandas, matplotlib, scikit-learn, tensorflow, bootcamp, udemy-course

## Member repositories
- mrdbourke/zero-to-mastery-ml (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:51.119357+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 2026-08-29T17:41:39.369305+00:00, confidence not recorded.
  - readme: https://github.com/mrdbourke/zero-to-mastery-ml (fetched 2026-08-28T04:09:51.119357+00:00, sha d0ffb123eb99)
  - homepage: https://dbourke.link/ZTMmlcourse (fetched 2026-08-29T08:37:36.131824+00:00, sha dae21b428ca4)
  - site_page: https://zerotomastery.io/about/instructor/andrei-neagoie (fetched 2026-08-29T08:37:36.141362+00:00, sha e838c6952ab9)
  - site_page: https://zerotomastery.io/about/instructor/daniel-bourke (fetched 2026-08-29T08:37:36.143465+00:00, sha dc0f7a8a3d8e)
- Data as of 2026-08-30T08:39:29.467469+00:00.