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What is this?
A collection of Jupyter Notebook code implementations for every algorithm covered in Li Hang's book 'Statistical Learning Methods' (《统计学习方法》, 2nd edition), organized chapter by chapter.
A collection of Jupyter Notebook code implementations for every algorithm covered in Li Hang's book 'Statistical Learning Methods' (《统计学习方法》, 2nd edition), organized chapter by chapter.Is this a library I install, or just code examples to read alongside the book?It's companion code for the book, not a packaged library: notebooks are organized one folder per chapter so you read and run them next to the text.
It's companion code for the book, not a packaged library: notebooks are organized one folder per chapter so you read and run them next to the text.How do I use this?Clone the repo and open the notebook for the chapter you're studying in Jupyter; each chapter (perceptron, k-NN, decision tree, SVM, etc.) has its own folder.
Clone the repo and open the notebook for the chapter you're studying in Jupyter; each chapter (perceptron, k-NN, decision tree, SVM, etc.) has its own folder.Is this actively kept up to date?The last noted update was in June 2020; the code was compiled once from community-contributed notes rather than continuously maintained since.
The last noted update was in June 2020; the code was compiled once from community-contributed notes rather than continuously maintained since.What topics does the code cover chapter by chapter?Chapters 2-12 cover supervised methods (perceptron, k-NN, naive Bayes, decision trees, logistic regression, SVM, boosting, EM, HMM, CRF), and chapters 13-22 cover unsupervised methods (clustering, SVD, PCA, LSA, PLSA, MCMC, LDA, PageRank).
Chapters 2-12 cover supervised methods (perceptron, k-NN, naive Bayes, decision trees, logistic regression, SVM, boosting, EM, HMM, CRF), and chapters 13-22 cover unsupervised methods (clustering, SVD, PCA, LSA, PLSA, MCMC, LDA, PageRank).Who put together and curates the code?It was compiled by the WeChat account '机器学习初学者' (Machine Learning Beginners) from notes and code contributed by community members, with a related study group ('黄博的机器学习圈子') mentioned for further discussion.
It was compiled by the WeChat account '机器学习初学者' (Machine Learning Beginners) from notes and code contributed by community members, with a related study group ('黄博的机器学习圈子') mentioned for further discussion.Why do people use this repo for interview prep?The README notes that many ML training courses and internet-company interview and written-exam questions are based on this book, so working through the matching code is a common way to prepare.
The README notes that many ML training courses and internet-company interview and written-exam questions are based on this book, so working through the matching code is a common way to prepare.Do I still need to read Li Hang's actual book to understand the code?Yes - this repo only provides the code; the math derivations, explanations, and theory live in the book itself and aren't duplicated here.
Yes - this repo only provides the code; the math derivations, explanations, and theory live in the book itself and aren't duplicated here.What are similar projects?Statistical-Learning-Method_Code, The-Art-Of-Programming-By-July-2nd, Machine-Learning, 100-Days-Of-ML-Code, Book4_Power-of-Matrix, autumn-recruitment, DeepLearning, PRML
Statistical-Learning-Method_Code, The-Art-Of-Programming-By-July-2nd, Machine-Learning, 100-Days-Of-ML-Code, Book4_Power-of-Matrix, autumn-recruitment, DeepLearning, PRMLWhich topics is it in?svm, statistics, interview-preparation, algorithms, computer-science, jupyter notebook, data-science, book, beginner-friendly, learning-resources, china, tutorial
svm, statistics, interview-preparation, algorithms, computer-science, jupyter notebook, data-science, book, beginner-friendly, learning-resources, china, tutorial