{"id":17723404,"url":"https://github.com/vivek3141/ml","last_synced_at":"2025-05-07T14:21:44.461Z","repository":{"id":32945325,"uuid":"145070465","full_name":"vivek3141/ml","owner":"vivek3141","description":"Easy to use high level python library for popular machine learning algorithms. 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It also has in-built support for graphing and optimizers based in C.\n\nLearn the module here:\n* [YouTube](https://www.youtube.com/watch?v=ReMIzozsx8Y)\n* [Blog Post](https://vivek3141.github.io/blog/posts/ml.html)\n* [Examples](https://github.com/vivek3141/ml/tree/master/examples)\n\nThis module uses a tensorflow backend.\n\n## Implemented Algorithms\n* 2D CNN `ml.cnn`\n* Basic MLP `ml.nn`\n* K-Means `ml.k_means`\n* Linear Regression `ml.linear_regression`\n    * optimized with C\n* Logistic Regression `ml.logistic_regression`\n* Graph Modules `ml.graph`\n    * Graph any function with or without data points - `from ml.graph import graph_function, graph_function_and_data`\n* Nonlinear Regression `ml.regression`\n* Optimizers - `ml.optimizer` optimized with C\n    * GradientDescentOptimizer - `from ml.optimizer import GradientDescentOptimizer`\n    * AdamOptimizer - `from ml.optimizer import AdamOptimizer`\t\n* \u003cb\u003eUNSTABLE\u003c/b\u003e - Character generating RNN - `ml.rnn`\n\n#### You can find examples for all of these in `/examples`\n\n### Pip installation\n```bash\npip install ml-python\n```\n### Python installation\n```bash\ngit clone https://github.com/vivek3141/ml\ncd ml\npython setup.py install\n```\n### Bash Installation\n```bash\ngit clone https://github.com/vivek3141/ml\ncd ml\nsudo make install\n```\n\n## Examples\nExamples for all implemented structures can be found in `/examples`. \u003cbr\u003e\nIn this example, linear regression is used.\n\u003cbr\u003e\u003cbr\u003e\nFirst, import the required modules.\n```python\nimport numpy as np\nfrom ml.linear_regression import LinearRegression\n```\nThen make the required object\n```python\nl = LinearRegression()\n```\nThis code below randomly generates 50 data points from 0 to 10 for us to run linear regression on.\n```python\n# Randomly generating the data and converting the list to int\nx = np.array(list(map(int, 10*np.random.random(50))))\ny = np.array(list(map(int, 10*np.random.random(50))))\n```\nLastly, train it. Set `graph=True` to visualize the dataset and the model.\n\n```python\nl.fit(data=x, labels=y, graph=True)\n```\n![Linear Regression](https://raw.githubusercontent.com/vivek3141/ml/master/images/linear_regression.png)\u003cbr\u003e\u003cbr\u003e\nThe full code can be found in `/examples/linear_regression.py`\n## Makefile\nA Makefile is included for easy installation.\u003cbr\u003e\nTo install using make run\n```bash\nsudo make\n```\nNote: Superuser privileges are only required if python is installed at `/usr/local/lib`\n## License\nAll code is available under the [MIT License](https://github.com/vivek3141/ml/blob/master/LICENSE.md)\n\n## Contributing\nPull requests are always welcome, so feel free to create one. Please follow the pull request template, so\nyour intention and additions are clear.\n## Contact\nFeel free to contact me by:\n* Email: vivnps.verma@gmail.com\n* GitHub Issue: [create issue](https://github.com/vivek3141/ml/issues/new)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvivek3141%2Fml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvivek3141%2Fml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvivek3141%2Fml/lists"}