100-Days-Of-ML-Code

100-Days-Of-ML-Code (Chinese edition) is a translated and adapted version of Avik-Jain's "100 Days of ML Code" challenge, presented as a study plan for learning machine learning over roughly a hundred days. The repository itself is not a piece of software you run; it is a day-by-day learning log written mostly in Simplified Chinese, with code in Jupyter notebooks, infographics, and links to external resources for each day's topic.

The structure follows the typical machine-learning curriculum and is organised into two top-level sections. Under supervised learning, the days cover data preprocessing, simple and multiple linear regression, logistic regression and the math behind it, k-Nearest Neighbours, Support Vector Machines including the kernel trick, Naive Bayes, decision trees and random forests, with code implementations using Scikit-Learn. Under unsupervised learning the project covers K-means and hierarchical clustering. Interleaved with the algorithm days are study days that point to outside courses and videos: Coursera's deep learning specialization, Bloomberg's machine learning course, Yaser Abu-Mostafa's Caltech course CS156, and the 3Blue1Brown YouTube channel for linear algebra and calculus. Later days include deep-learning foundation notebooks using Python, TensorFlow and Keras, web-scraping practice with Beautiful Soup, and NumPy study from Jake VanderPlas's Python Data Science Handbook.

You would use this repository if you read Chinese and want a structured, opinionated curriculum that mixes algorithm-by-algorithm coding exercises with curated outside videos and courses. Sample datasets are included.