Are you starting or planning to experiment with machine learning? Sooner or later, you must choose a machine learning tool to work on any problem. In any robotic process automation or intelligent automation scenario, using a right tool is more essential as, if not less than, using correct algorithms.
Though there exist enterprise-ready platforms like WorkFusion to automate your business processes yet open source tools work good enough for beginners. And thanks to the growing recognition of machine learning, the list of tools is impressively long. Among those, I am going to introduce you to the best open source tools to help you get started quickly.
Why use machine learning tools?
Machine learning tools help solve a machine learning problem in an easy, fast, and fun way. These tools provide all essentials including algorithms that help you make a jumpstart with your solution and avoid most of the common pitfalls. That said, these tools lower the barrier for beginners to create useful solutions, thus adding fun to the work.
Machine learning tools can even automate steps in the applied machine learning, thus speeding up the whole process. Also, their algorithms are already tried and tested by others so you will find them faster and better in comparison to custom techniques.
Moreover, good machine learning tools are much more than just fast algorithm libraries. They offer a set of resources and incorporate best practices for creating machine learning projects in an efficient, standard manner. Last but not the least, good tools are backed by a trusted community or organization and provide support to early developers.
What are the best open source tools?
The best open-source machine learning tools are developed by reputable communities around the world and avail handsome support for bug fixes and your questions. These tools are also free from common limitations, allowing you to use them as required.
PredictionIO is a machine learning server from the Apache Foundation for creating predictive engines to solve machine learning problems. It comes bundled with Apache Spark, MLlib, HBase, Spray, and Elasticsearch — packaging these all into one to form a complete platform for getting started with machine learning easily.
TensorFlow is a machine learning framework by Google. This software library allows building any computational model or neural network — from text to image classifiers, to more advanced models and networks. Though TensorFlow is comparatively hard for beginners yet its popularity and developer support makes it an ideal pick.
Keras is a high-level library built on top of other frameworks like Amazon’s MXNet, Google’s TensorFlow, Microsoft’s CNTK, and more. It eases the process of drafting new models and networks — making it as easy as creating methods in Python. Moreover, it helps keep your project modular by offering common neural building blocks.
Shogun is a multi-platform machine learning library with support for various languages including C#, Java, Python, Scala, and many more. It is an efficient library which offers standard as well as advanced machine learning algorithms and facilitates rapid prototyping, thanks to its algorithm classes and general purpose tools.
MLlib is an efficient, scalable machine learning library for the Apache Spark. It supports multiple languages like Java, Python, R, and Scala. This library contains numerous algorithms like other machine learning libraries and performs 100x faster than MapReduce. Moreover, you can run and deploy it on any cluster and data of Hadoop.
Source: Deep Learning on Medium