https://github.com/SmallVagetable/machine_learning_python

通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。实现算法有KNN、Kmeans、EM、Perceptron、决策树、逻辑回归、svm、adaboost、朴素贝叶斯
https://github.com/SmallVagetable/machine_learning_python

Last synced: 3 months ago
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通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。实现算法有KNN、Kmeans、EM、Perceptron、决策树、逻辑回归、svm、adaboost、朴素贝叶斯

• Host: GitHub
• URL: https://github.com/SmallVagetable/machine_learning_python
• Owner: SmallVagetable
• Created: 2019-01-10T15:07:46.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-10-22T11:12:50.000Z (over 4 years ago)
• Last Synced: 2024-01-17T01:05:51.600Z (5 months ago)
• Language: Python
• Homepage:
• Size: 2.96 MB
• Stars: 705
• Watchers: 28
• Forks: 301
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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• awesome-stars-copy - SmallVagetable/machine_learning_python - 通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。实现算法有KNN、Kmeans、EM、Perceptron、决策树、逻辑回归、svm、adaboost、朴素贝叶斯 (Python)

README

        
# machine-learning

通过阅读网上的资料代码，进行自我加工，努力实现常用的机器学习算法。

# 目前已经实现可运行算法

### [KNN和KdTree的实现](https://github.com/SmallVagetable/machine_learning_python/tree/master/knn)

### [感知机的基本形式和对偶形式的实现](https://github.com/SmallVagetable/machine_learning_python/tree/master/perceptron)

### [Kmeans和Kmeans++的实现](https://github.com/SmallVagetable/machine_learning_python/tree/master/kmeans)

### [EM GMM高斯混合和GMM+LASSO的实现](https://github.com/SmallVagetable/machine_learning_python/tree/master/em)

### [实现朴素贝叶斯的基本算法和高斯混合朴素贝叶斯算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/naive_bayes)

### [实现决策树的基本算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/decision_tree)

### [实现adaboost基本算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/adaboost)

### [实现svm基本算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/support_vector_machine)

### [实现逻辑回归基本算法](https://github.com/SmallVagetable/machine_learning_python/tree/master/logistic_regression)