Verified project record

MLEveryday/100-Days-Of-ML-Code

A 100-day Chinese-language machine learning curriculum that provides translated lessons, Python code examples using Scikit-Learn, and conceptual infographics. It is designed for readers who need a structured, self-paced educational resource covering foundational supervised and unsupervised learning algorithms.

Project overview

It translates the 100 Days of ML Code track into Chinese, providing supervised and unsupervised learning materials alongside Scikit-Learn code.

Project type: Data Processing
Use cases: Learning & Education
Deployment: Refer to project documentation
License: MIT

Best for

Chinese-language educators and general users needing a self-paced, 100-day curriculum covering foundational machine learning concepts.
Learners who benefit from visual aids and executable Scikit-Learn Python code examples alongside translated text.

Key capabilities

Provides a day-by-day Chinese translation of the 100 Days of ML Code learning track, covering supervised and unsupervised learning algorithms.
Includes Python code examples and Jupyter notebooks using libraries like Scikit-Learn for algorithms such as data preprocessing, linear regression, SVM, and K-NN.
Provides visual information graphics explaining machine learning concepts like data preprocessing, simple linear regression, and decision trees.

Limitations and risks

GPU support, minimum hardware specifications, operating system compatibility, data boundary constraints, cost dependencies, and telemetry are not documented.

Getting started

Setup difficulty is rated as easy because the project consists of static educational text and code snippets requiring no build steps. Users only need a Markdown viewer or a Jupyter environment to begin reading the translated lessons and viewing the linked Python code.

Evidence and sources

GitHub project description: 100-Days-Of-ML-Code中文版
README: - 有监督学习 - [数据预处理](#数据预处理--第1天) - [简单线性回归](#简单线性回归--第2天) - [多元线性回归](#多元线性回归--第3天) - [逻辑回归](#逻辑回归--第4天) - [k近邻法(k-NN)](#k近邻法k-nn--第7天) - [支持向量机(SVM)](#支持向量机svm--第12天) - [决策树](#决策树--…
README: 今天我在线性相关数据上实现了SVM。使用Scikit-Learn库。在scikit-learn中我们有SVC分类器，我们用它来完成这个任务。将在下一次实现时使用kernel-trick。Python代码见[此处](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Co…
README: 使用Scikit-Learn库实现了SVM算法以及内核函数，该函数将我们的数据点映射到更高维度以找到最佳超平面。
README: [数据预处理实现](https://github.com/MachineLearning100/100-Days-Of-ML-Code/blob/master/Code/Day%201_Data_Preprocessing.md) <p align="center"> <img src="https://github.com/MachineLearning…

Reviewed at: 2026-07-17T11:24:48Z