https://github.com/tobegit3hub/ml_implementation
Implementation of Machine Learning Algorithms
https://github.com/tobegit3hub/ml_implementation
implementation machine-learning python
Last synced: 10 months ago
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Implementation of Machine Learning Algorithms
- Host: GitHub
- URL: https://github.com/tobegit3hub/ml_implementation
- Owner: tobegit3hub
- License: mit
- Created: 2017-06-11T11:44:27.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-03-01T02:35:26.000Z (almost 7 years ago)
- Last Synced: 2025-03-29T19:04:13.146Z (10 months ago)
- Topics: implementation, machine-learning, python
- Language: Python
- Size: 34.2 KB
- Stars: 405
- Watchers: 13
- Forks: 187
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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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/)