ml-python 2.1

The easiest way to do machine learning

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
• Blog Post

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

pip install ml-python

Python installation

git clone https://github.com/vivek3141/ml
cd ml
python setup.py install

Bash Installation

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.
In this example, linear regression is used.

First, import the required modules.

import numpy as np
from ml.linear_regression import LinearRegression

Then make the required object

l = LinearRegression()

This code below randomly generates 50 data points from 0 to 10 for us to run linear regression on.

# 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.

l.fit(data=x, labels=y, graph=True)

The full code can be found in /examples/linear_regression.py

Makefile

A Makefile is included for easy installation.
To install using make run

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

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: [e-mail]
• GitHub Issue: create issue