# towardsai/tutorials

AI-related tutorials. Access any of them for free → https://towardsai.net/editorial

Repository: https://github.com/towardsai/tutorials
Canonical: https://ross.abutalabs.com/products/towardsai-tutorials
Homepage: https://towardsai.net/editorial
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Topics: machine-learning, data-science, deep-learning, neural-networks, math, nlp, python, programming, python-tutorial, tutorial, mathematics, google-colab, monte-carlo-simulation, linear-algebra, recommendation-system, collaborative-filtering, sentiment-analysis
Last push: 2024-05-04T15:16:01+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": 2246, "days_push": 851, "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 1026, forks 364 (observed 2026-08-28T04:03:16.754150+00:00)

## What it is
A collection of free AI, machine learning, and data science tutorials by Towards AI, provided as Jupyter Notebook code examples with companion articles. Topics span neural networks, NLP, linear algebra, probability, Monte Carlo simulation, and recommendation systems.

## Use cases
- learn machine learning algorithms with python code examples
- build neural networks from scratch
- learn nlp with python
- understand linear algebra for deep learning
- monte carlo simulation tutorial
- build a recommendation system with collaborative filtering
- learn probability and statistics with python
- sentiment analysis tutorial

## When to choose
- you want free, code-first tutorials with accompanying math explanations
- you are a beginner learning ML, NLP, or data science fundamentals in Python
- you prefer Jupyter/Colab notebooks you can run and modify

## When to avoid
- you need production-grade, maintained software libraries rather than educational code
- you want a structured course with certification rather than standalone tutorials
- you need content under a fully permissive license - articles are proprietary, only code is MIT

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, nlp, data-science, math
- domain: machine-learning, data-science, deep-learning, tutorials, mathematics
- platform: python, cross-platform
- tags: jupyter-notebooks, tutorials, google-colab, neural-networks, linear-algebra, monte-carlo-simulation, recommendation-systems, sentiment-analysis, beginner-friendly, natural-language-processing

## Member repositories
- towardsai/tutorials (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:16.754150+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 2026-08-30T07:08:00.275560+00:00, confidence not recorded.
  - readme: https://github.com/towardsai/tutorials (fetched 2026-08-28T04:03:16.754150+00:00, sha 9851f2924316)
  - homepage: https://towardsai.net/editorial (fetched 2026-08-29T13:08:33.127493+00:00, sha ca6006ea8324)
  - site_page: https://towardsai.com/academy/about (fetched 2026-08-29T13:08:33.135032+00:00, sha ad6a5640bf5a)
- Data as of 2026-08-30T08:39:29.467469+00:00.