Top 10 Python, AI and Machine Learning Open Source Projects

March 21, 2018, 5:22 p.m. By: Kirti Bakshi

AI and Machine Learning

It is not an easy task to get into Machine Learning and AI. Given the enormous amount of resources that are available today, many aspiring professionals and enthusiasts find it hard to establish a proper path into the field. The field is evolving at a constant pace and it is crucial that we keep up with this rapid development.

In order to cope with the speed of evolution and innovation that is today so overwhelming, a good way to stay updated and knowledgeable on the advances that have taken place in ML is to engage with the community by contributing to the many open-source projects and tools that are used daily by advanced professionals.

Today, we discuss top 10 open-source projects on Python, Machine Learning and AI. For more information to the same, links have been mentioned as well.

1. TensorFlow:

TensorFlow is an open source software library that makes the use of data flow graphs for the purpose of numerical computation. Nodes represent mathematical operations in the graph, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture allows its users to deploy computation without rewriting code to one or more CPUs or GPUs in a desktop, server, or mobile device. TensorFlow also includes a data visualization toolkit called TensorBoard.

GitHub: Tensorflow

2. Scikit-learn:

Scikit-learn for machine learning is a module in Python that has been built on the top of SciPy. The project was started in the year 2007 as a Google Summer of Code project, by David Cournapeau and since then there have been many who have contributed. It is currently being maintained by a team of volunteers.

GitHub: Scikit-Learn

3. Keras:

Keras that is written in Python is a high-level neural networks API, that is capable of running both on top of either TensorFlow or Theano. It was developed keeping its main focus on enabling fast experimentation.

Keras can be used if you require a deep learning library that:

  • Through user friendliness, modularity, and extensibility allows for easy and fast prototyping.

  • Supports both convolutional networks, recurrent networks, as well as combinations of the two.

  • Seamlessly Runs on CPU and GPU.

GitHub: Keras

4. PyTorch:

PyTorch is a package in Python that provides its users with the below features:

  • With strong GPU acceleration, computation of Tensor

  • The building of Deep neural networks on an autograd system that is tape-based

Python packages such as NumPy, SciPy and Cython can be reused to extend PyTorch when needed.

GitHub: PyTorch

5. Theano:

Theano is a library written in Python that allows its users to define, optimize, as well as evaluate mathematical expressions that efficiently involve arrays that are multi-dimensional in nature. It can in order to perform efficient symbolic differentiation use GPUs.

GitHub: Theano

6. Gensim:

Gensim is a library in Python for document indexing topic modelling and similarity retrieval with large corpora. The Target audience is information retrieval (IR) and natural language processing (NLP) community.


  • All algorithms w.r.t. the corpus size is independent of the memory.

  • Interfaces that are Intuitive

  • Easy to plug in one's own input corpus/datastream (trivial streaming API)

  • Easy to extend with other Vector Space algorithms (trivial transformation API)

  • Efficient multicore implementations of algorithms that are quite popular, such as Latent Dirichlet Allocation (LDA), Random Projections (RP) and many more.

  • Distributed computing: On a cluster of computers, has the ability to run Latent Semantic Analysis and Latent Dirichlet Allocation

GitHub: Gensim

7. Caffe:

Caffe is a deep learning framework made with keeping in mind expression, speed, as well as modularity. The framework has been developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) as well as community contributors.

GitHub: Caffe

8. Chainer:

Chainer is a deep learning framework framework that is Python-based and aims at flexibility. It based on the define-by-run approach (a.k.a. dynamic computational graphs) provides automatic differentiation APIs as well as object-oriented high-level APIs to build as well as train neural networks.

It using CuPy also supports CUDA/cuDNN for high-performance training and inference.

GitHub: Chainer

9. Statsmodels:

Statsmodels is a package in Python that for statistical computations provides its users with a complement to Scipy that includes descriptive statistics, estimation as well as inference for statistical models.

GitHub: Statsmodel

10. Shogun:

Shogun is Machine learning toolbox which provides its users with a wide range of unified as well as efficient Machine Learning (ML) methods. The toolbox very seamlessly allows the combination of multiple data representations, algorithm classes, as well as general purpose tools.

Github: Shogun