# ml_implementation

**Repository Path**: elegyprincess/ml_implementation

## Basic Information

- **Project Name**: ml_implementation
- **Description**: Implementation of Machine Learning Algorithms
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-01-16
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Implementation of Machine Learning

## Introduction

These are the implementation of machine learning algorithms **from scratch**.

* [x] [Linear_regression](./linear_regression/)
  * [x] [Least square method](./linear_regression/least_square_method.py)
* [x] [Logistic regression](./logistic_regression/)
  * [x] [Logistic regression](./logistic_regression/logistic_regression.py)
  * [x] [Distributed logistic regression](./logistic_regression/distributed_logistic_regression.py)
* [x] [Bayes](./bayes/)
  * [x] [Navie bayes](./bayes/navie_bayes.py)
* [x] [Decision tree](./decision_tree/)
  * [x] [C4.5 decision tree](./c45_decision_tree/decision_tree.py)
  * [x] [ID3 decision tree](./id3_decision_tree/decision_tree.py)
  * [ ] Random forest
  * [ ] Gradient boosting tree
* [x] [Neural network](./neural_network/)
  * [x] [Perception](./neural_network/perception.py)
  * [x] [Neural network](./neural_network/neural_network.py)
* [x] [SVM](./svm/)
  * [x] [SVM](./svm/svm.py)
* [ ] [FM](./fm/)
* [ ] [Random forest](./random_forest/)
* [ ] [KMeans](./kmeans/)
* [ ] [Condictional random field](./condictional_random_field/)
  * [ ] [Condictional random field](./condictional_random_field/condictional_random_field.py)
* [ ] [Others](./others/)
  * [ ] [Sigmoid](./others/sigmoid/)
  * [ ] [Standard deviation](./others/standard_deviation/)
  * [ ] [Normal distribution](./others/normal_distribution/)
  * [ ] [Auto gradient](./others/autogradient/)
  * [x] [Random walk](./others/random_walk/)