Jack-Cherish/Machine-Learning resource

Health v2 · maintenance only

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10378 stars · 5065 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A Python3 code companion and tutorial series for the classic 'Machine Learning in Action' book, implementing kNN, decision trees, naive Bayes, logistic regression, SVM, linear/tree regression, and AdaBoost from scratch. It is paired with extensive Chinese-language blog articles by Jack Cui explaining the theory and practice of each algorithm.

Use cases

• learn machine learning algorithms from scratch in python
• implement knn and decision trees without sklearn
• understand svm and smo algorithm derivation with code
• study naive bayes text classification example
• practice logistic regression with gradient ascent
• follow a machine learning in action study guide
• learn cart regression trees and pruning

When to choose

• you want to learn classic ML algorithms by hand-coding them in Python3
• you prefer tutorial-style explanations paired with runnable example code
• you are studying the 'Machine Learning in Action' book and want updated Python3 code

When to avoid

• you need a production ML library - use scikit-learn instead
• you need deep learning, neural networks, or GPU acceleration
• you require a maintained, licensed package with pip installation and tests

Facets

learning-resource · maturity maintenance

machine-learning developer-tools machine-learning tutorials education python cross-platform knn decision-tree naive-bayes logistic-regression svm adaboost regression python3 machine-learning-in-action chinese algorithms

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