# DeqianBai/Hands-on-Machine-Learning

A series of Jupyter notebooks with Chinese comment that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

Repository: https://github.com/DeqianBai/Hands-on-Machine-Learning
Canonical: https://ross.abutalabs.com/products/hands-on-machine-learning
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Last push: 2022-10-03T16:35:33+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": 2876, "days_push": 1430, "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 1546, forks 438 (observed 2026-08-28T04:05:01.622003+00:00)

## What it is
A collection of Jupyter notebooks with Chinese comments that walk through the fundamentals of machine learning and deep learning in Python using Scikit-Learn and TensorFlow, based on the book 'Hands-on Machine Learning with Scikit-Learn and TensorFlow'. It combines the book's code examples with text explanations so learners can study without switching between the book and code.

## Use cases
- learn machine learning fundamentals through hands-on notebooks
- study deep learning with tensorflow in chinese
- get a fast systematic introduction to ML with scikit-learn
- practice ML exercises with solutions for interview prep
- follow a full ML project walkthrough from data to model
- learn neural networks CNNs RNNs and reinforcement learning basics

## When to choose
- you prefer Chinese-language explanations while learning ML
- you want book-style explanations merged directly into runnable notebooks
- you need a structured path covering both classical ML and deep learning
- you have limited time and want a fast but systematic ML introduction

## When to avoid
- you need up-to-date coverage of the latest TensorFlow or scikit-learn APIs
- you want English-language learning material
- you are already an expert looking for advanced topics
- you need production-ready ML code rather than educational examples

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, tutorials, education
- platform: python, cross-platform
- tags: jupyter-notebooks, chinese, scikit-learn, tensorflow, hands-on-ml, study-notes

## Member repositories
- DeqianBai/Hands-on-Machine-Learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:01.622003+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 2026-08-30T04:30:31.827633+00:00, confidence not recorded.
  - readme: https://github.com/DeqianBai/Hands-on-Machine-Learning (fetched 2026-08-28T04:05:01.622003+00:00, sha 142adaccce93)
- Data as of 2026-08-30T08:39:29.467469+00:00.