A 37k-star Chinese companion to Andrew Ng’s 2014 ML course

What it does

This repository is the open-source edition of a single engineer’s Chinese notes for Andrew Ng’s 2014 Stanford machine learning course. It contains week-by-week Markdown and HTML transcripts, original PPT slides, bilingual Chinese-English subtitles, and Python code samples. The author also hosts an online HTML version and provides a PDF in the repository root for direct download.

The interesting bit

Rather than a sparse cheat sheet, the project treats the video lectures as a structured textbook: every week is transcribed and annotated, from linear regression and backpropagation to SVM kernels and anomaly detection. The author began translating the English subtitles himself in late 2014, and the notes have since been downloaded tens of thousands of times.

Key highlights

• Complete ten-week syllabus coverage matching the original Coursera course, including supervised learning, unsupervised learning, and applied best-practice advice.
• Multiple formats: Markdown source, HTML with math formulas rendered as images, Word documents, and a root-level PDF.
• Supplementary assets: original course PPTs, bilingual SRT subtitles, and a code folder with Python implementations.
• Online reading hosted at ai-start.com so you can browse without cloning.
• References to Chinese-language textbooks such as Li Hang’s Statistical Learning Methods and Zou Bo’s machine learning course.

Caveats

• Video files are not included; the README points to a Baidu Cloud link for the MP4s.
• Math formulas in the HTML edition are baked into images, so you need the Markdown source to edit or copy the equation LaTeX.
• Week 2 still covers the original Octave/MATLAB tutorial, though Python code is provided elsewhere in the repo.

Verdict

Chinese-speaking students working through Andrew Ng’s classic ML course will find this an exhaustive, well-indexed study aid; if you are not following that specific curriculum, it is essentially a very thorough set of translated lecture notes.