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https://github.com/veb-101/machine-learning-algorithms
One notebook to learn it all - Algorithms from scratch
https://github.com/veb-101/machine-learning-algorithms
matplotlib numpy pandas python-3-7 scikit-learn scipy seaborn
Last synced: about 2 months ago
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One notebook to learn it all - Algorithms from scratch
- Host: GitHub
- URL: https://github.com/veb-101/machine-learning-algorithms
- Owner: veb-101
- Created: 2019-09-29T17:24:05.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-05-26T15:06:41.000Z (over 4 years ago)
- Last Synced: 2023-03-05T12:33:16.017Z (almost 2 years ago)
- Topics: matplotlib, numpy, pandas, python-3-7, scikit-learn, scipy, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 2.7 MB
- Stars: 7
- Watchers: 1
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Machine Learning Algorithms
[](https://mybinder.org/v2/gh/veb-101/Machine-Learning-Algorithms/master) [](https://colab.research.google.com/github/veb-101/Machine-Learning-Algorithms/blob/master/) [](https://github.com/veb-101/Machine-Learning-Algorithms/issues) [](https://github.com/veb-101/Machine-Learning-Algorithms/pulls)
[](https://repl.it/repls/folder/machine%20learning%20algorithms?ref=button)
---
#### Numpy implementation of some basic machine learning algorithms
---
##### Run Online
| Sr. No. | Algorithm | View | Python File | Colab Notebook |
| ------- | ----------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 1.a | KNN - Classification | [notebook](https://nbviewer.jupyter.org/github/veb-101/Machine-Learning-Algorithms/blob/master/K-Nearest%20Neigbors/KNN-Classifier.ipynb) | [.py](https://repl.it/@VaibhavSingh4/1a-k-NN-classification) | [](https://colab.research.google.com/github/veb-101/Machine-Learning-Algorithms/blob/master/K-Nearest%20Neigbors/KNN-Classifier.ipynb) |
| 1.b | KNN - Regression | [notebook](https://nbviewer.jupyter.org/github/veb-101/Machine-Learning-Algorithms/blob/master/K-Nearest%20Neigbors/KNN-Regression.ipynb) | [.py](https://repl.it/@VaibhavSingh4/1b-k-NN-Regression) | [](https://colab.research.google.com/github/veb-101/Machine-Learning-Algorithms/blob/master/K-Nearest%20Neigbors/KNN-Regression.ipynb) |
| 1.c | KNN - Weighted Classification | [notebook](https://nbviewer.jupyter.org/github/veb-101/Machine-Learning-Algorithms/blob/master/K-Nearest%20Neigbors/KNN_weighted_classification.ipynb) | [.py](https://repl.it/@VaibhavSingh4/1c-KNN-weighted-classification) | [](https://colab.research.google.com/github/veb-101/Machine-Learning-Algorithms/blob/master/K-Nearest%20Neigbors/KNN_weighted_classification.ipynb) |
| 2 | Linear Regression | [notebook](https://nbviewer.jupyter.org/github/veb-101/Machine-Learning-Algorithms/blob/master/Linear%20Regression/linear_regression.ipynb) | | [](https://colab.research.google.com/github/veb-101/Machine-Learning-Algorithms/blob/master/Linear%20Regression/linear_regression.ipynb) |
| 3 | Logistic Regression | [notebook](https://nbviewer.jupyter.org/github/veb-101/Machine-Learning-Algorithms/blob/master/Logistic%20Regression/logistic%20regression.ipynb) | [.py](https/repl.it/@VaibhavSingh4/Logistic-Regression) | [](https://colab.research.google.com/github/veb-101/Machine-Learning-Algorithms/blob/master/Logistic%20Regression/logistic%20regression.ipynb) |
| 4 | Decision Trees | [notebook](https://nbviewer.jupyter.org/github/veb-101/Machine-Learning-Algorithms/blob/master/Decision%20Trees/decision%20tree.ipynb) | [.py](https://repl.it/@VaibhavSingh4/decision-tree) | [](https://colab.research.google.com/github/veb-101/Machine-Learning-Algorithms/blob/master/Decision%20Trees/decision%20tree.ipynb) |
| 5 | Naive Bayes | [notebook](https://nbviewer.jupyter.org/github/veb-101/Machine-Learning-Algorithms/blob/master/Naive%20Bayes/Naive%20Bayes.ipynb) | | [](https://colab.research.google.com/github/veb-101/Machine-Learning-Algorithms/blob/master/Naive%20Bayes/Naive%20Bayes.ipynb) |
| 6 | Random Forest | [notebook](https://nbviewer.jupyter.org/github/veb-101/Machine-Learning-Algorithms/blob/master/Random%20Forest/random_forest.ipynb) | | [](https://colab.research.google.com/github/veb-101/Machine-Learning-Algorithms/blob/master/Random%20Forest/random_forest.ipynb) |
| 7 | AdaBoost | [notebook](https://nbviewer.jupyter.org/github/veb-101/Machine-Learning-Algorithms/blob/master/Boosting%20-%20AdaBoost/adaboost.ipynb) | | [](https://colab.research.google.com/github/veb-101/Machine-Learning-Algorithms/blob/master/Boosting%20-%20AdaBoost/adaboost.ipynb) |
| 8 | K-means Clustering | [notebook](https://nbviewer.jupyter.org/github/veb-101/Machine-Learning-Algorithms/blob/master/K-means/K_means.ipynb) | [.py](https://repl.it/@VaibhavSingh4/k-means) | [](https://colab.research.google.com/github/veb-101/Machine-Learning-Algorithms/blob/master/K-means/K_means.ipynb) |
| 9 | PCA | [notebook](https://nbviewer.jupyter.org/github/veb-101/Machine-Learning-Algorithms/blob/master/Principal%20Component%20Analysis/dimensionality%20reduction.ipynb) | | [](https://colab.research.google.com/github/veb-101/Machine-Learning-Algorithms/blob/master/Principal%20Component%20Analysis/dimensionality%20reduction.ipynb) |
| 10 | Apriori Algorithm | | [.py](https://repl.it/@VaibhavSingh4/Apriori-Algorithm) | |