# Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow

Notes & exercise solutions of Part I from the book: "Hands-On ML with Scikit-Learn, Keras & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems" by Aurelien Geron

Repository: https://github.com/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow
Canonical: https://ross.abutalabs.com/products/hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow
Homepage: https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/tree/master/
Language: Jupyter Notebook
License Family: other
Topics: machine-learning, deep-learning, artificial-intelligence, neural-networks, scikit-learn, notebooks
Last push: 2024-04-18T11:17:33+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": 2532, "days_push": 867, "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 1060, forks 420 (observed 2026-08-28T04:03:25.797106+00:00)

## What it is
A collection of Jupyter notebooks containing notes and exercise solutions for Part I of Aurelien Geron's book 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'. It covers ML fundamentals including classification, SVMs, decision trees, ensembles, and unsupervised learning using scikit-learn.

## Use cases
- learn machine learning fundamentals from scratch
- study alongside the Hands-On ML book
- find solutions to Hands-On ML chapter exercises
- review scikit-learn concepts like SVMs and decision trees
- get runnable notebook examples for classic ML algorithms
- prepare for ML interviews with core concepts

## When to choose
- you are reading the Hands-On ML book and want companion notes and solutions
- you prefer learning through hands-on Jupyter notebook examples
- you want a structured roadmap through ML fundamentals with scikit-learn

## When to avoid
- you need production ML code or a maintained library
- you want coverage of the deep learning (Keras/TensorFlow) part of the book, which is not included
- you need a resource with an explicit open-source license

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, artificial-intelligence, data-science, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, scikit-learn, tensorflow, keras, book-notes, exercise-solutions, education

## Member repositories
- Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:25.797106+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 2026-08-30T06:56:53.403884+00:00, confidence not recorded.
  - readme: https://github.com/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow (fetched 2026-08-28T04:03:25.797106+00:00, sha cb4f8c088fee)
  - homepage: https://nbviewer.jupyter.org/github/Akramz/Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow/tree/master/ (fetched 2026-08-29T12:58:50.736341+00:00, sha 9db4d768b048)
  - site_page: https://nbviewer.org/faq (fetched 2026-08-29T12:58:50.745291+00:00, sha f62b276c6d7d)
- Data as of 2026-08-30T08:39:29.467469+00:00.