Metadata-Version: 2.1
Name: mlpractice
Version: 0.0.2
Summary: MLpractice is a course, in which you will learn about the most effective machine learning techniques,and gain practice implementing them.
Home-page: https://github.com/avalur/mlpractice
License: MIT
Keywords: machinelearning,deeplearning,ml,dl,practice,mlpractice,machine,deep
Author: Vladislav Ushakov
Author-email: uvd2001@mail.ru
Requires-Python: >=3.7,<=3.9
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Dist: ipython (>=7.30.1,<8.0.0)
Requires-Dist: jupyter (>=1.0.0,<2.0.0)
Requires-Dist: numpy (>=1.21.4,<2.0.0)
Requires-Dist: scipy (>=1.7.2,<2.0.0)
Requires-Dist: torch (>=1.10.0,<2.0.0)
Description-Content-Type: text/markdown

# <div align="center">MLpractice</div>
<a href='https://mlpractice.readthedocs.io/en/latest/?badge=latest'>
    <img src='https://readthedocs.org/projects/mlpractice/badge/?version=latest' alt='Documentation Status' />
</a>

MLpractice 🚀 is a course, in which you will learn about the most effective machine learning techniques, and gain practice implementing them.

## <div align="center">Documentation</div>

See the [MLpractice Docs](https://mlpractice.readthedocs.io/en/latest/?badge=latest) for full documentation on course task functions.

## <div align="center">Quick Start</div>

<details open>
<summary>Install</summary>
  
### Pip
Pip install it in a [**Python>=3.7.0**](https://www.python.org/) environment.
```bash
pip install mlpractice
```

<!-- ### Clone and install
Clone repo and install [requirements.txt](https://github.com/avalur/mlpractice/blob/main/requirements.txt) in a
[**Python>=3.7.0**](https://www.python.org/) environment.

```bash
git clone https://github.com/avalur/mlpractice  # clone
cd mlpractice
pip install -r requirements.txt  # install
``` -->

</details>

<details open>
<summary>Init</summary>

Make a course folder with tasks by simply running
```bash
mlpractice init
```

</details>

## <div align="center">Contact</div>

For MLpractice bugs and feature requests please visit [GitHub Issues](https://github.com/avalur/mlpractice/issues).

</div>

