ml-python 1.6

The easiest way to do machine learning

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

pip install ml-python

Or by cloning the repo and installing it.

git clone https://github.com/vivek3141/ml
cd ml
python setup.py 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, we will see how to learn a linear regression example.

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
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