# machine_learning_python

**Repository Path**: guzhipin/machine_learning_python

## Basic Information

- **Project Name**: machine_learning_python
- **Description**: 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
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-12-07
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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)