# lazyprogrammer/machine_learning_examples

A collection of machine learning examples and tutorials.

Repository: https://github.com/lazyprogrammer/machine_learning_examples
Canonical: https://ross.abutalabs.com/products/machine_learning_examples
Homepage: https://lazyprogrammer.me
Language: Python
License Family: other
Topics: deep-learning, machine-learning, reinforcement-learning, python, natural-language-processing, data-science
Last push: 2026-04-27T00:30:32+00:00

## Health v2 (maintenance only)
Score: 68/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 79, release rhythm 35, longevity 100
- inputs: {"age_days": 4416, "days_push": 129, "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 8916, forks 6404 (observed 2026-08-28T04:10:26.276588+00:00)

## What it is
A large collection of machine learning, deep learning, NLP, and reinforcement learning example code and tutorials accompanying Lazy Programmer's online courses. Code is organized in folders, one per course, with some newer examples hosted on Google Colab.

## Use cases
- learn machine learning with python examples
- study deep learning course code
- learn reinforcement learning in pytorch
- find NLP and transformer examples
- practice time series forecasting with machine learning
- supplement online AI and data science courses

## When to choose
- you are taking one of Lazy Programmer's courses and need the companion code
- you want hands-on Python examples across ML, deep learning, NLP, and RL
- you prefer learning by reading and running tutorial code

## When to avoid
- you need a production-ready library with an API or package releases
- you want a single coherent project rather than many independent course folders
- you need licensed, reusable code (no license is provided)

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, reinforcement-learning, nlp, data-science
- domain: machine-learning, deep-learning, data-science, tutorials
- platform: python
- tags: tutorials, course-code, example-code, education, natural-language-processing

## Member repositories
- lazyprogrammer/machine_learning_examples (main) score 68

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:26.276588+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 2026-08-29T17:24:33.826270+00:00, confidence not recorded.
  - readme: https://github.com/lazyprogrammer/machine_learning_examples (fetched 2026-08-28T04:10:26.276588+00:00, sha 056fb0b660d2)
  - homepage: https://lazyprogrammer.me (fetched 2026-08-29T08:24:53.140908+00:00, sha 4589d37ec79e)
  - site_page: https://lazyprogrammer.me/about (fetched 2026-08-29T08:24:53.143658+00:00, sha df580cd963ad)
- Data as of 2026-08-30T08:39:29.467469+00:00.