Top 5 Python Libraries for Machine learning Projects

With day-to-day observations, it is simply observed that machines are becoming more intelligent to pick out recurring patterns and make better decisions without any human intervention. Python language has always come up with varieties in their new versions. One of the most appreciated features is Python Decorators as a modern framework for easy interface. Among all, the Python developers are getting higher benefits from the open source tools which makes it faster to familiarize with this language and adjust accordingly.

In this post, we are going to explore top 5 open source libraries of Python programming language to use in your next Machine learning projects.

• Chainer

Chainer is a Python-based standalone open source framework for creating your own machine learning projects. By implementing a full range of machine learning model including state of the art model such as recurrent neural networks and variational auto encoders by providing a flexible, intuitive, and high performance. It runs on defined by the run approach by training a network in two phases of fixed connection and actual training calculations.

Contributors: 202 

Commits: 23487

Github URL: Chainer

• Deap (Distributed Evolutionary Algorithms in Python)

For rapid prototyping and testing of ideas in a novel evolutionary computation framework, the Deap is the one. Works in perfect harmony with parallelization mechanisms such as multiprocessing and SCOOP allowing to make transparent algorithms and data structures. Using the most common evolutionary techniques such as genetic algorithm, evolution strategies, differential evolution, particle swarm optimization and estimation of distribution algorithm.

Contributors: 41

Commits: 1972

Github URL: Deap

• Gensim

With the loaded features such as scalable statistical semantics, analyze plain text documents for semantic structure and retrieve semantically similar documents; the Gensim is a free Python library for the developers. It is a robust open source vector space modeling and topic modeling toolkit implemented in Python. Can be cited over commercial and academic applications in a diverse array of disciplines.

Contributors: 301

Commits: 3689

Github URL: Gensim

• Keras

It is regarded as one of the best libraries for learning how to use Python in machine learning for beginners. Keras provide datasets by processing utilities and compiling models for easy neural network expression. It is a high-level neural network API written in Python and also capable of running other frameworks like Tensorflow, CNTK, or Theano.

Contributors: 762

Commits: 4957

Github URL: Keras

• TensorFlow

Google developers built Tensorflow which is an open source computational framework to express algorithms that involve multiple Tensor operations simply because neural networks can be presented in form of computational graphs. You can also implement other Python programming language as there is access to the underlying C++ API.

Contributors: 1771

Commits: 46532

Github URL: Tensorflow

Final Thoughts

For some time now, Python has continued to dominate the web development world. Owing to the explosion of machine learning you can utilize Python language helps you build your own ML algorithms. If you are looking to create your own machine learning project, the above list of best Python libraries should be given a start.