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The easiest way to do machine learning

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[![Build Test](]( [![PyPi Version](]( [![Python Compatibility](]( [![License](]( # 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 cd ml python install ` ### Bash Installation `bash git clone 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, labels=y, graph=True) ` ![Linear Regression](<br><br> The full code can be found in /examples/ ## 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]( ## Contact Feel free to contact me by: * Email: * GitHub Issue: [create issue](

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