Open Source Machine Learning Libraries For Python Developers

Machine Learning is a branch of computer science and artificial intelligence, that gives machines the ability to learn by itself to make machines more intelligence. In recent years, Python programming language has gained more popularity in the field of machine learning, thanks to its easy-to-learn syntax and a dozen of machine learning libraries written for it. In this blog, I will share you a comprehensive list of Python Machine Learning libraries.
  1. PML 
    PML is a Python machine learning library consisting of a number of out-of-the-box modules, such as - decision tree, KNN, clustering, and Naive Bayes. It's a lightweight library that fits for small projects.

  2. PyML
    PyML is an object-oriented machine learning library designed for Linux and Mac OS X users with a focus on SVMs and kernel functions.

  3. Theanets
    Theanets is a deep learning and neural networks library working well with other excellent libraries, such as -NumPy and Scikit-learn.

  4. Scikit-learn
    Scikit-learn is a well-documented and effective machine learning library that makes data mining and data analysis a breeze.

  5. NeuroLab
    NeuroLab is a flexible library for neural networks and machine learning. Its interface is similar to Neural Network Toolbox for MATLAB.

  6. PyClustering
    PyClustering is a data mining and neural network library that provides implementations for both, Python and C++.

  7. NimbleNet
    NimbleNet is a lightweight and effective library for creating a feed-forward neural network.

  8. TensorFlow
    TensorFlow is probably the most popular machine learning library which is released by Google. It's a symbolic math package but also can be used as a distributed machine learning library.

  9. Keras
    Keras is a high-level deep learning framework written in Python that can be running on top of most famous libraries such as TensorFlow, CNTK, and Theano.
     
  10. PyTorch
    PyTorch is a full-fledged machine learning and deep learning library released by Facebook. It features a NumPy-like Tensor library and a number of libraries for different algorithms in machine learning.

  11. MLPy
    MLPy is an open source Python library for machine learning built on top of NumPy, SciPy, and GNU Scientific Libraries. It has a wide range of algorithms that allow you to utilize the state-of-the-art techniques to implement supervised and unsupervised learning. It works with both, Python 2 and Python 3.

  12. PMLL
    PMLL is a Python library with an objective to make machine learning easy, inspired by R and Matlab.

  13. PyBrain
    PyBrain provides a powerful toolbox to make machine learning tasks effective and flexible. It covers the algorithms for neural networks, reinforcement learning, evolving computing, and unsupervised learning.

  14. Caffe
    Caffe is a modular and effective deep learning framework created by Berkeley AI Research. It's one of fastest Convolution Neural Networks implementation in the world, which can process more than 60M images per day. It has a huge developers community that constantly contributes code to make it better.

  15. NNABLA
    NNABLA is a neural network library open-sourced by Sony aims to make research, development, and implementation of neural network easily.

  16. Pylearn2
    Pylearn2 is a machine learning library built on top of Theano, with a host of machine learning algorithms enable you to do effective machine learning tasks.

  17. CNTK
    CNTK is Microsoft's deep learning toolkit.It combines most common-use models in machine learning like Convolution Neural Nets, Recurrent nets,feed-forward DNNs to build a comprehensive toolbox.It means you can easily implement your machine learning systems a piece of cake.
That's all.Thanks for your interest. 
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