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What is this?
A Chinese-language tutorial project that implements classic machine learning algorithms in Python3 - kNN, Decision Tree, Naive Bayes, Logistic Regression, SVM, Regression, Regression Tree and AdaBoost - following the exercises in the book 'Machine Learning in Action', with companion blog articles explaining each chapter.
A Chinese-language tutorial project that implements classic machine learning algorithms in Python3 - kNN, Decision Tree, Naive Bayes, Logistic Regression, SVM, Regression, Regression Tree and AdaBoost - following the exercises in the book 'Machine Learning in Action', with companion blog articles explaining each chapter.Is this an installable Python package or just example scripts?It is not a pip-installable library - it is a folder-per-chapter collection of standalone Python3 scripts and accompanying blog-post write-ups that you read through and run individually.
It is not a pip-installable library - it is a folder-per-chapter collection of standalone Python3 scripts and accompanying blog-post write-ups that you read through and run individually.How do I run the code?Clone the repo and run the script for whichever algorithm you want directly with python3 (e.g. the kNN, decision tree, SVM or AdaBoost scripts in their own folders); there is no build step or package to install.
Clone the repo and run the script for whichever algorithm you want directly with python3 (e.g. the kNN, decision tree, SVM or AdaBoost scripts in their own folders); there is no build step or package to install.Do I need scikit-learn or other ML libraries installed?Most algorithms are implemented from scratch with plain Python/NumPy, but a few chapters (like the SVM digit-recognition example and the AdaBoost comparison) also show the same task solved with scikit-learn.
Most algorithms are implemented from scratch with plain Python/NumPy, but a few chapters (like the SVM digit-recognition example and the AdaBoost comparison) also show the same task solved with scikit-learn.Is this still actively maintained?The repo is tied to a 2017 blog-post series covering the 'Machine Learning in Action' chapters; the final clustering section is marked as 'more exciting content coming soon', suggesting the project was never fully extended beyond AdaBoost/regression/regression-tree.
The repo is tied to a 2017 blog-post series covering the 'Machine Learning in Action' chapters; the final clustering section is marked as 'more exciting content coming soon', suggesting the project was never fully extended beyond AdaBoost/regression/regression-tree.What algorithms are covered?kNN, Decision Tree, Naive Bayes, Logistic Regression, SVM (simplified and full SMO, plus kernel SVM), AdaBoost, linear regression, and CART regression trees - each with worked examples like dating classification, lens prescription, spam filtering, horse-colic mortality prediction, and used-Lego-price prediction.
kNN, Decision Tree, Naive Bayes, Logistic Regression, SVM (simplified and full SMO, plus kernel SVM), AdaBoost, linear regression, and CART regression trees - each with worked examples like dating classification, lens prescription, spam filtering, horse-colic mortality prediction, and used-Lego-price prediction.Where can I find the written explanations for each chapter?Each chapter links to a matching article on the author's personal website, CSDN and Zhihu, with companion videos on Bilibili and updates posted through the author's WeChat public account.
Each chapter links to a matching article on the author's personal website, CSDN and Zhihu, with companion videos on Bilibili and updates posted through the author's WeChat public account.Is this based on a specific book?Yes, it follows the exercises from 'Machine Learning in Action' (《机器学习实战》), reimplemented in Python3 instead of the book's original Python2 code.
Yes, it follows the exercises from 'Machine Learning in Action' (《机器学习实战》), reimplemented in Python3 instead of the book's original Python2 code.What are similar projects?ailearning, python-machine-learning-book, lihang-code, Statistical-Learning-Method_Code, 100-Days-Of-ML-Code, machine-learning-zoomcamp, mml-book.github.io, Complete-Python-3-Bootcamp
ailearning, python-machine-learning-book, lihang-code, Statistical-Learning-Method_Code, 100-Days-Of-ML-Code, machine-learning-zoomcamp, mml-book.github.io, Complete-Python-3-BootcampWhich topics is it in?svm, from-scratch, scikit-learn, classification, students, python3, examples, algorithms, course, data-science, book, artificial-intelligence
svm, from-scratch, scikit-learn, classification, students, python3, examples, algorithms, course, data-science, book, artificial-intelligence