Finding the best machine learning tools can be an overwhelming endeavor. It is such a vast, new, and ever-evolving field, that developers find themselves spending as much time studying the software options as they spend studying the technology itself.
No matter what you intend to build, what programming language you use, or how experienced you are, there is a free and open-source machine learning tool out there that suits your needs. Here are 12 of the best chosen by the experts.
Topics Covered:
- TensorFlow
- Scikit-learn
- PyTorch
- Weka
- Colab
- Accord.Net
- KNIME
- Keras
- OpenNN
- Apache Mahout
- Shogun
- Orange3
TensorFlow
TensorFlow was originally created by the Google Brain team, and is still used by Google, but has since become free and open-source. It is a machine learning software library most commonly used for the inference and training of deep neural networks. Get started with TensorFlow at https://www.tensorflow.org/
Scikit-learn
Scikit-learn is a free and open-source Python library for machine learning. It features a plethora of algorithms used for clustering, classification, regression, and more. It’s designed to work with other libraries commonly used for data science such as SciPy and Numpy.
PyTorch
PyTorch is another Python library commonly used in machine learning. It was originally created by Facebook’s AI Research lab but is now free and open-source. PyTorch is predominantly used for natural language processing and computer vision.
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Weka
Weka is a free and open-source machine learning software developed by the University of Waikato in New Zealand. It was designed for the book “Data Mining: Practical Machine Learning Tools and Techniques” and specializes in predictive modeling and data analysis.
Colab
Google’s Colaboratory, better known as ‘Colab’, is a browser-based Python platform that is fully functional within the browser. In other words, a user can write and execute code, without downloading any additional software, using a Jupyter notebook. The service is free and available for anyone to use.
Accord.NET
Rather than just a single library or tool, Accord.NET is an entire machine learning framework. It’s not only free and open-source, but can even be used for commercial use under the Gnu Lesser Public License, version 2.1. Accord.NET is written in C# and is most commonly used for statistics, computer vision, computer audition, and signal processing.
KNIME
Rather than a library, KNIME is a machine-learning platform that focuses on data analytics and reporting. It was originally a proprietary product, produced by the University of Konstanz, but has since been released as free and open-source. It is most commonly used for pharmaceutical research but has a wide array of other business intelligence use cases as well.
Keras
Keras is another free and open-source Python library popularly used for machine learning. It was designed by Google engineer François Chollet and acts as an interface for the TensorFlow library. Keras is used primarily for deep neural networks.
OpenNN
OpenNN is a free and open-source neural networks library, written in C++, used for machine learning and, more specifically, deep learning. OpenNN can be used in regression, classification, forecasting, and association. It was created by the International Center for Numerical Methods in Engineering but is currently being developed by the AI startup Artelnics.
Apache Mahout
Apache Mahout is a free and open-source project created by the Apache Software Foundation. It is designed to create machine learning algorithms, mostly focused on linear algebra, that is highly scalable. Past versions used Adobe Hadoop to do this but today it mostly uses Apache Spark.
Shogun
Shogun is another free and open-source machine learning library written in C++ but offering support for Python, R, Ruby, Java, C#, and many other programming languages. It offers support for a wide variety of things, from regression and classification to Hidden Markov models, and is primarily used for education and research.
Orange3
Orange3 is a completely free and open-source machine-learning toolkit for Python. It’s used for everything from data mining and visualization to preprocessing and modeling. It has an easy-to-use canvas interface making it a favorite of those newer to machine learning. However, it also has advanced features seasoned developers would need.
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