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.

### Pip installation

```pip install ml-python
```

### Git installation

```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`

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