Metadata-Version: 2.1
Name: ml-python
Version: 2.2
Summary: The easiest way to do machine learning
Home-page: https://github.com/vivek3141/ml
Author: Vivek Verma
Author-email: vivekverma3141@gmail.com
License: MIT
Description: [![Build Test](https://travis-ci.com/vivek3141/ml.svg?branch=master)](https://travis-ci.com/vivek3141/ml)
        [![Downloads](https://pepy.tech/badge/ml-python)](https://pepy.tech/project/ml-python)
        [![PyPi Version](https://img.shields.io/pypi/v/ml-python.svg)](https://pypi.python.org/pypi/ml-python)
        [![Python Compatibility](https://img.shields.io/pypi/pyversions/ml-python.svg)](https://pypi.python.org/pypi/fastai)
        [![License](https://img.shields.io/pypi/l/ml-python.svg)](https://pypi.python.org/pypi/ml-python)
        # ML
        
        This module provides for the easiest way to implement Machine Learning algorithms without the need to know about them.
        
        Learn the module here:
        * [YouTube](https://www.youtube.com/watch?v=ReMIzozsx8Y)
        * [Blog Post](https://vivek3141.github.io/blog/posts/ml.html)
        
        Use this module if
        - You are a complete beginner to Machine Learning.
        - You find other modules too complicated.
        
        This module is not meant for high level tasks, but only for simple use and learning.
        
        I would not recommend using this module for big projects.
        
        This module uses a tensorflow backend.
        
        ### Pip installation
        ```bash
        pip install ml-python
        ```
        ### Python installation
        ```bash
        git clone https://github.com/vivek3141/ml
        cd ml
        python setup.py install
        ```
        ### Bash Installation
        ```bash
        git clone https://github.com/vivek3141/ml
        cd ml
        sudo make install
        ```
        This module has support for ANNs, CNNs, linear regression, logistic regression, k-means.
        
        ## Examples
        Examples for all implemented structures can be found in `/examples`. <br>
        In this example, linear regression is used.
        <br><br>
        First, import the required modules.
        ```python
        import numpy as np
        from ml.linear_regression import LinearRegression
        ```
        Then make the required object
        ```python
        l = LinearRegression()
        ```
        This code below randomly generates 50 data points from 0 to 10 for us to run linear regression on.
        ```python
        # Randomly generating the data and converting the list to int
        x = np.array(list(map(int, 10*np.random.random(50))))
        y = np.array(list(map(int, 10*np.random.random(50))))
        ```
        Lastly, train it. Set `graph=True` to visualize the dataset and the model.
        
        ```python
        l.fit(data=x, labels=y, graph=True)
        ```
        ![Linear Regression](https://raw.githubusercontent.com/vivek3141/ml/master/images/linear_regression.png)<br><br>
        The full code can be found in `/examples/linear_regression.py`
        ## Makefile
        A Makefile is included for easy installation.<br>
        To install using make run
        ```bash
        sudo make
        ```
        Note: Superuser privileges are only required if python is installed at `/usr/local/lib`
        ## License
        All code is available under the [MIT License](https://github.com/vivek3141/ml/blob/master/LICENSE.md)
        
        ## Contributing
        Pull requests are always welcome, so feel free to create one. Please follow the pull request template, so
        your intention and additions are clear.
        ## Contact
        Feel free to contact me by:
        * Email: vivnps.verma@gmail.com
        * GitHub Issue: [create issue](https://github.com/vivek3141/ml/issues/new)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 2
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
