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The easiest way to do machine learning

Project description

[![Build Test](https://travis-ci.com/vivek3141/ml.svg?branch=master)](https://travis-ci.com/vivek3141/ml) [![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.

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 ` 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) ## Contact Feel free to contact me by: * Email: vivnps.verma@gmail.com * GitHub Issue: [create issue](https://github.com/vivek3141/ml/issues/new)

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