# Nyandwi/machine_learning_complete

A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.

Repository: https://github.com/Nyandwi/machine_learning_complete
Canonical: https://ross.abutalabs.com/products/machine_learning_complete
Homepage: https://nyandwi.com/machine_learning_complete/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: machine-learning, deep-learning, data-science, neural-networks, computer-vision, nlp, tensorflow, keras, scikit-learn, pandas, matplotlib, seaborn, python, numpy, data-analysis, open-source, data-visualization, datascience
Last push: 2023-09-22T22:11:36+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": 1803, "days_push": 1076, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5038, forks 834 (observed 2026-08-28T04:09:08.772801+00:00)

## What it is
A comprehensive open-source machine learning learning package containing 35 interactive Jupyter notebooks covering Python, data analysis, classical ML, deep learning, computer vision, and NLP. Notebooks are beginner-friendly, visual, and runnable in Colab or Deepnote.

## Use cases
- learn machine learning from scratch with notebooks
- study deep learning for computer vision and nlp
- refresh python and pandas for data analysis
- understand classical ml algorithms like regression and clustering
- find a free structured ml curriculum
- learn feature engineering and data cleaning techniques
- get an introduction to mlops

## When to choose
- you are a beginner wanting a comprehensive, hands-on ML course in notebooks
- you prefer learning with visuals and runnable Colab examples
- you want coverage from Python basics through deep learning in one place

## When to avoid
- you need production-grade ML code or a maintained library
- you want the latest cutting-edge research or LLM-focused content
- you need an actively updated resource (last release was 2023)

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, data-science, data-visualization, nlp, computer-vision
- domain: machine-learning, deep-learning, data-science, computer-vision, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, tensorflow, scikit-learn, pandas, beginner-friendly, mlops, educational, natural-language-processing

## Member repositories
- Nyandwi/machine_learning_complete (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:08.772801+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 2026-08-29T18:17:25.134172+00:00, confidence not recorded.
  - readme: https://github.com/Nyandwi/machine_learning_complete (fetched 2026-08-28T04:09:08.772801+00:00, sha fa6b56c2ea23)
  - homepage: https://nyandwi.com/machine_learning_complete/ (fetched 2026-08-29T08:57:58.803873+00:00, sha a000fa2c7c43)
  - site_page: https://nyandwi.com/machine_learning_complete/08_encoding_categorical_features (fetched 2026-08-29T08:57:58.812906+00:00, sha 3bfa081ebcc9)
  - site_page: https://nyandwi.com/machine_learning_complete/09_feature_scaling (fetched 2026-08-29T08:57:58.814769+00:00, sha 15e84dedf19a)
- Data as of 2026-08-30T08:39:29.467469+00:00.