The easiest way to do machine learning

# ML

This module provides for the easiest way to implement Machine Learning algorithms. It also has in-built support for graphing and optimizers based in C.

Learn the module here:

This module uses a tensorflow backend.

## Implemented Algorithms

• 2D CNN `ml.cnn`
• Basic MLP `ml.nn`
• K-Means `ml.k_means`
• Linear Regression `ml.linear_regression`
• optimized with C
• Logistic Regression `ml.logistic_regression`
• Graph Modules `ml.graph`
• Graph any function with or without data points - `from ml.graph import graph_function, graph_function_and_data`
• Nonlinear Regression `ml.regression`
• Optimizers - `ml.optimizer` optimized with C
• GradientDescentOptimizer - `from ml.optimizer import GradientDescentOptimizer`
• AdamOptimizer - `from ml.optimizer import AdamOptimizer`
• UNSTABLE - Character generating RNN - `ml.rnn`

### 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
```

## 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`

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:

## Project details

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