machinelearning
My blogs and code for machine learning. http://cnblogs.com/pinard
Runnable Jupyter notebook collection paired with a Chinese machine learning blog, covering classical ML, deep learning, NLP, and reinforcement learning with scikit-learn examples.
This repository is the companion code collection for a popular Chinese-language machine learning blog written by Liu Jianping (known as Pinard) at cnblogs.com/pinard. Each entry in the repository pairs with a blog article and provides a working code example for the concept the article explains. The README is written entirely in Chinese.
The collection spans most of the core machine learning curriculum. It includes notebooks on regression, classification, clustering, dimensionality reduction, and ensemble methods. There are also sections on the mathematics behind machine learning (including matrix calculus and Markov Chain Monte Carlo methods), as well as natural language processing, deep learning, recommendation systems, and reinforcement learning topics such as Q-Learning, Deep Q-Networks (DQN), Actor-Critic, and AlphaGo Zero.
Most of the code is written as Jupyter Notebooks using Python with scikit-learn and pandas, which are standard tools for data science in Python. Older articles from 2016 to 2017 used Python 2.7, while articles from 2018 onward switched to Python 3.6. A note in the README mentions that some of the older code was rewritten in Python 3.6 to fill gaps, so minor differences may exist between the notebooks and the blog text for those older articles.
This is a learning and reference resource, not a deployable product. It is useful for someone reading the accompanying Chinese blog posts and wanting to run the examples, or for anyone who wants worked code illustrations of classical machine learning algorithms alongside mathematical explanations.
Where it fits
- Run worked code examples alongside Chinese blog articles explaining machine learning algorithms from first principles.
- Study classical ML algorithms such as regression, clustering, and ensemble methods with matching scikit-learn implementations.
- Explore reinforcement learning implementations including DQN, Actor-Critic, and AlphaGo Zero with runnable Python notebooks.