Jack-Cherish/Machine-Learning Alternatives

For an actively maintained Jupyter-notebook implementation of the same classic algorithms in Python.

Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.

For classic ML algorithms implemented in C++ instead of Python if language matters to you.

machine learning algorithm

For an importable MIT-licensed minimal Python ML library rather than copying tutorial snippets.

Simple machine learning library / 簡單易用的機器學習套件

For a much larger Chinese ML/DL learning hub with translations and extra topics, despite no recent pushes.

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

For a structured 100-day ML code challenge with a different learning path and broader examples.

100 Days of ML Coding

For a maintained C#/.NET library implementing many classic ML algorithms instead of Python educational code.

Machine learning for C# .Net

For a minimal GPL-licensed Python reference of classic ML implementations, though it has no book-style walkthroughs and is unmaintained.

经典机器学习算法的极简实现

For a standalone kNN implementation from the same book if you only need that algorithm.

机器学习实战

For a standalone AdaBoost implementation written from scratch when only that algorithm is needed.

Implementation of AdaBoost algorithm in Python

For a Python SVM multiclass example when you need only SVM rather than the full algorithm tour.

用Python实现SVM多分类器

For a Java decision-tree library when you just need decision-tree modeling in Java.

A fast and easy to use decision tree learner in java

For a broader from-scratch Python catalog including extra models, but it has not been maintained for years.

机器学习(Machine Learning, ML)python简洁实现，包括混合高斯模型，KMeans，决策树，随机森林，K近邻，线性判别分析，逻辑斯蒂回归（梯度下降法，牛顿法），多层感知机（分类+回归），Naive Bayes（离散+高斯），多分类SVM，线性回归，隐马尔可夫模型（包括前向算法，后向算法，Viterbi算法和BaumWelch算法），主成分分析等模型

For an ID3 decision tree in JavaScript when you need browser/Node code and only that algorithm.

Small JavaScript implementation of ID3 Decision tree

For a maintained Python framework of ID3/C4.5/CART/boosting tree algorithms, but it has a narrower tree-only scope.

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

For a Julia CART/random-forest package when you work in Julia and need tree ensembles only.

Julia implementation of Decision Tree (CART) and Random Forest algorithms

For distributed kNN on Spark instead of the seed's toy Python kNN, if you only need that algorithm.

k-Nearest Neighbors algorithm on Spark

For a JavaScript SVM implementation with kernels if your target is browser/Node, but it covers only SVM.

Support Vector Machine in Javascript (SMO algorithm, supports arbitrary kernels) + GUI demo

For an ID3 decision-tree implementation in Ruby when you prefer Ruby and need only decision trees.

ID3-based implementation of the ML Decision Tree algorithm

For numpy implementations from the Watermelon book if you follow that textbook, though unmaintained.

numpy 实现的 周志华《机器学习》书中的算法及其他一些传统机器学习算法

For from-scratch naive-Bayes and AdaBoost used in spam filtering, if those two algorithms are your focus.

:email: Implement Naive Bayes and Adaboost from scratch and use them to filter spam emails.

For high-performance boosted trees in Julia when you are working in Julia and only need tree ensembles.

Boosted trees in Julia

For a focused Jupyter tutorial on decision trees and random forests if you do not need the other algorithms.

I've demonstrated the working of the decision tree-based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample. All the steps have been explained in detail with graphics for better understanding.