https://github.com/voorhs/ml-practice
ML algorithms implementation
https://github.com/voorhs/ml-practice
aimasters lomonosov-msu machine-learning numpy python
Last synced: 3 months ago
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ML algorithms implementation
- Host: GitHub
- URL: https://github.com/voorhs/ml-practice
- Owner: voorhs
- Created: 2021-10-11T17:27:45.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-01-02T01:50:23.000Z (over 1 year ago)
- Last Synced: 2024-12-29T00:12:23.026Z (5 months ago)
- Topics: aimasters, lomonosov-msu, machine-learning, numpy, python
- Language: Jupyter Notebook
- Homepage:
- Size: 31 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Machine Learning Algorithms
Own implementation of machine learning algorithms:
| Algorithm | Code | Experiments | Report | Source |
| ---------------------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| linear regression | [reg.py](https://github.com/voorhs/Machine-Learning-Algorithms/blob/main/linear-regression/reg.py) | [self-reg.ipynb](https://github.com/voorhs/Machine-Learning-Algorithms/blob/main/linear-regression/self-reg.ipynb) | — | Andrew Ng’s old course, [new one](https://www.coursera.org/learn/machine-learning?specialization=machine-learning-introduction) |
| binary logistic regression | [logistic.py](https://github.com/voorhs/Machine-Learning-Algorithms/blob/main/binary-logistic-regression/logistic.py) | [self-log.ipynb](https://github.com/voorhs/Machine-Learning-Algorithms/blob/main/binary-logistic-regression/self-log.ipynb) | — | Andrew Ng’s old course, [new one](https://www.coursera.org/learn/machine-learning?specialization=machine-learning-introduction) |
| one vs all logistic regression | [classification.py](https://github.com/voorhs/Machine-Learning-Algorithms/blob/main/onevsall-logistic-regression/classification.py) | [classification.ipynb](https://github.com/voorhs/Machine-Learning-Algorithms/blob/main/onevsall-logistic-regression/classification.ipynb) | — | Andrew Ng’s old course, [new one](https://www.coursera.org/learn/machine-learning?specialization=machine-learning-introduction) |
| k-means | [clust.py](https://github.com/voorhs/Machine-Learning-Algorithms/blob/main/kmeans/clust.py) | [clust.ipynb](https://github.com/voorhs/Machine-Learning-Algorithms/blob/main/kmeans/clust.ipynb) | — | [Alexander Dyakonov’s mini-course](https://github.com/Dyakonov/IML/blob/master/2020/IML2020_04cluster_01.pdf) |
| knn | [knn](https://github.com/voorhs/Machine-Learning-Algorithms/tree/main/knn/knn) | [experiments.ipynb](https://github.com/voorhs/Machine-Learning-Algorithms/blob/main/knn/experiments.ipynb) | — | [AIMasters ML course](https://github.com/voorhs/Machine-Learning-Algorithms/blob/main/knn-classification/task.pdf) |
| SGD | [sgd](https://github.com/voorhs/Machine-Learning-Algorithms/tree/main/sgd/gd) | [experiments.ipynb](https://github.com/voorhs/Machine-Learning-Algorithms/blob/main/sgd/experiments.ipynb) | [report.pdf](https://github.com/voorhs/Machine-Learning-Algorithms/blob/main/sgd/report.pdf) | [University assignment](https://github.com/mmp-practicum-team/mmp_practicum_fall_2022/blob/main/Tasks/Task%2002/task_02.pdf) |
| decision tree | [tree.ipynb](https://github.com/voorhs/Machine-Learning-Algorithms/blob/main/decision-tree/tree.ipynb) | — | — | AIMasters ML course |
| random forest and gradient boosting regression | [ensembles.py](https://github.com/voorhs/flask-ensembles/blob/main/src/ensembles.py) | [experiments.ipynb](https://github.com/voorhs/flask-ensembles/blob/main/src/experiments.ipynb) | [report.pdf](https://github.com/voorhs/flask-ensembles/blob/main/report.pdf) | [University assignment](https://github.com/mmp-practicum-team/mmp_practicum_fall_2022/blob/main/Tasks/Task%2003/task_03.pdf) |
| GLAD (EM algo) | [glad.py](https://github.com/voorhs/Machine-Learning-Algorithms/tree/main/em-glad/glad.py) | [derivation_experiments.ipynb](https://github.com/voorhs/Machine-Learning-Algorithms/tree/main/em-glad/derivation_experiments.ipynb) | — | [University assignment](https://github.com/mmp-mmro-team/-mmp_mmro_spring_2023/blob/main/homework_practice/em/homework-practice-09-em.ipynb) |
| word alignment (EM algo) | [em-word-alignment](https://github.com/voorhs/Machine-Learning-Algorithms/tree/main/em-word-alignment) | [derivation_experiments.ipynb](https://github.com/voorhs/Machine-Learning-Algorithms/tree/main/em-word-alignment/derivation_experiments.ipynb) | — | [University assignment](https://github.com/mmp-mmro-team/-mmp_mmro_spring_2023/blob/main/homework_practice/em/homework-practice-09-em.ipynb) |
| average precision subtleties | | [average-precision-comparison.ipynb](https://github.com/voorhs/ml-practice/blob/main/average-precision-comparision.ipynb) | — | me |
| PCA | [pca](https://github.com/voorhs/Machine-Learning-Algorithms/tree/main/pca/src.py) | [testbed.ipynb](https://github.com/voorhs/Machine-Learning-Algorithms/tree/main/pca/testbed.ipynb) | [pca](https://github.com/voorhs/Machine-Learning-Algorithms/tree/main/pca) | me |