Metadata-Version: 2.1
Name: ml-python
Version: 1.4
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: [![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 algoritms without the need to know about them.
        
        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.
        
        Install by running
        <br><br>
        ```bash
        pip install ml-python
        ```
        <br><br>
        Or by cloning the repo and installing it.<br>
        ```bash
        git clone https://github.com/vivek3141/ml
        cd ml
        python setup.py install
        ```
        <br><br>
        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, we will see how to learn a linear regression example.
        <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
        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://github.com/vivek3141/ml/blob/master/images/linear_regression.png)<br><br>
        The full code can be found in `/examples/linear_regression.py`
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
