mlforce 0.1.0

Easy-to-use toolkit with numpy, pandas, and PyTorch for beginners

Machine Learning Starter

Introduction

My library, named "MLStarter" which stands for Machine Learning Starter, is a comprehensive Python toolkit meticulously crafted to cater to the needs of machine learning beginners. With a user-friendly design, it aims to empower aspiring data enthusiasts and learners in the fields of machine learning and data analysis. Each module in "MLStarter" serves a distinct purpose, providing a seamless and efficient experience for users throughout the entire machine learning journey.

By combining ease of use with powerful functionality, this library aims to nurture your understanding and practical skills in machine learning, unlocking the doors to endless possibilities in the realm of data-driven solutions. So, embrace "MaLL" and embark on a rewarding journey of mastering the art of machine learning!

Installation

You can install MLStarter using pip:

pip install mlstarter

Quick Start

Here's a quick example of how to use MLStarter Library:

# Import modules
from mlstarter.datasets import StandardDataset
from mlstarter.mlsolutions import KNearestNeighbor

# Load the datasets
ds = StandardDataset()
ds.load_knn('numeric')
X, y = ds.data, ds.target
new_example = ds.new_example 

# Call a particular algorithm
knn = KNearestNeighbor(k=3)
knn.fit(X, y)
knn.predict(new_example)
print(knn)

# Ouputs:
# The closest nearest neighbors are ex.1, 0, 2. The majority of Label is yes; hence, 3-Nearest Neighbor predicts Label = yes.

Modules

• datasets module: The "datasets" module serves as a reliable data-loading component that efficiently handles various datasets and seamlessly feeds them to the models.
• mlsolutions The "mlsolutions" module is a comprehensive collection of essential algorithms in the fields of machine learning and data mining. It provides users with a powerful toolkit to tackle a wide range of challenges in these domains.
• mlperceptron The "mlperceptron" module is dedicated to implementing multi-layer perceptrons using the popular numerical computing library, NumPy.
• digitrecognizer The "digitrecognizer" module specializes in digit recognition tasks, such as the well-known MNIST dataset. It enables seamless training and evaluation of models for accurate digit classification.

Dependencies

• Python
• numpy
• pandas
• scipy
• torch

Documentation

For more detailed usage instructions, check out the Documentation.

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvement, please Open an issue or submit a pull request.

License

MaLL is licensed under the MIT License.

Version History

• v0.1.0 (2023-07-28)
  • Initial release of MaLL.

Author and contact:

Author: Jiarui Xu
Email: [e-mail]