The easiest way to do machine learning
Project description
# 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 /> 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
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