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        "description": "========\n[ML]\u00b2 :  Machine Learning for Machine Learning\n========\n\n|contributors| |activity|\n\n.. |contributors| image:: https://img.shields.io/github/contributors/mlsquare/mlsquare.svg\n  :alt: contributors\n  :target: https://github.com/mlsquare/mlsquare/graphs/contributors\n\n.. |activity| image:: https://img.shields.io/github/commit-activity/m/mlsquare/mlsquare.svg\n  :alt: activity\n  :target: https://github.com/mlsquare/mlsquare/pulse\n\n.. |last_commit| image:: https://img.shields.io/github/last-commit/mlsquare/mlsquare.svg\n  :alt: last_commit\n  :target: https://github.com/mlsquare/mlsquare/commits/master\n\n.. |size| image:: https://img.shields.io/github/repo-size/mlsquare/mlsquare.svg\n  :alt: size\n\n\nMLSquare is an open source developer-friendly Python library, designed to make use of Deep Learning for Machine Learning developers.\n\n\n================\nGetting Started!\n================\n\nSetting up ``mlsquare`` is simple and easy\n\n    1. Create a Virtual Environment\n\n    .. code-block:: bash\n\n        virtualenv ~/.venv\n        source ~/.venv/bin/activate\n\n    2. Install ``mlsquare`` package\n\n    .. code-block:: bash\n\n        pip install mlsquare\n\n    3. Import ``dope`` function from ``mlsquare`` and pass the ``sklearn`` model object\n\n    .. code-block:: python\n\n        >>> from mlsquare.imly import dope\n        >>> from sklearn.linear_model import LinearRegression\n        >>> from sklearn.preprocessing import StandardScaler\n        >>> from sklearn.model_selection import train_test_split\n        >>> import pandas as pd\n\n        >>> model = LinearRegression()\n        >>> data = pd.read_csv('./datasets/diabetes.csv', delimiter=\",\",\n                       header=None, index_col=False)\n        >>> sc = StandardScaler()\n        >>> data = sc.fit_transform(data)\n        >>> data = pd.DataFrame(data)\n\n        >>> X = data.iloc[:, :-1]\n        >>> Y = data.iloc[:, -1]\n        >>> x_train, x_test, y_train, y_test =\n            train_test_split(X, Y, test_size=0.60, random_state=0)\n        >>> m = dope(model)\n\n        >>> # All sklearn operations can be performed on m, except that the underlying implementation uses DNN\n        >>> m.fit(x_train, y_train)\n        >>> m.score(x_test, y_test)\n\n================\nTutorial\n================\n\nFor a comprehensive tutorial please do checkout this `link`__\n\n__ https://github.com/mlsquare/mlsquare/blob/master/examples/imly.ipynb\n\n\n\nFor detailed documentation refer `documentation`__\n\n__ http://mlsquare.readthedocs.io\n\n\nWe would love to hear your feedback. Drop us a mail at *info*[at]*mlsquare.org*\n\n",
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