A Chinese-language, 100-day structured curriculum covering foundational machine learning and deep learning concepts with Python code implementations and infographics. The project is designed for educators and general users seeking a self-study track in core machine learning algorithms.
Project overview
It provides a specifically tailored Chinese translation of the 100 Days of ML Code challenge, covering supervised and unsupervised learning algorithms with accompanying Jupyter notebooks and conceptual infographics.
Project type
Data Processing · Learning Resources
Use cases
Learning & Education
Deployment
Refer to project documentation
License
MIT
Best for
Educators and general users seeking a Chinese-language, 100-day structured curriculum for foundational machine learning and deep learning concepts.
Learners who benefit from visual infographics and Python code examples using libraries like Scikit-Learn alongside translated educational 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
Requirements for GPU, minimum hardware, operating systems, databases, model requirements, and telemetry are not documented.
Cost dependencies and external services required for operation are not documented.
Getting started
Setup is rated as easy because the materials consist of static educational text and code snippets requiring no build steps. Users need only a Markdown viewer or a Jupyter environment to begin.
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