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12 Best Machine Learning Tools

Machine learning is a subfield of computer science evolved from the study of pattern recognition and computational learning theory in artificial intelligence. With it, users can explore the construction and study of algorithms.

It is fast becoming the area of interest of lot many people. With each passing day, more and more number of people are joining the queue of the ones who wish to learn about Machine Learning. Keeping the interest of people in mind, we keep coming up with articles revolving around the topic.

Previously we have talked in detail about 12 Best Free Ebooks for Machine Learning and 8 Best Machine Learning Cheat Sheets and today we are here with the list of best machine learning tools that promise to help you make the most of it. So, without wasting too much time check out all the best machine learning tools and increase your productivity.

1. Accord Framework/AForge.net

Accord is a machine learning and signal processing framework that provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. If you all have been previously introduced with AForge.NET Framework, this is a project that is the extension of the same promising to provide a more complete scientific computing environment.

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2. Scikit-learn

scikit-learn is a Python module for machine learning. It is built on top of SciPy and distributed under the 3-Clause BSD license.It is open source, accessible to everyone and reusable in different contexts.

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3. MLDemos

Created by Dr. Basilio Noris, MLDemos is an open-source visualization tool that people can use for machine learning algorithms. It helps users in studying and understanding how several algorithms function and how their parameters affect and modify the results.

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4. ConvNetJS

ConvNetJS is deep Learning in Javascript implementation of Neural networks, together with nice browser-based demos.

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5. Mahout

The Apache Mahout is a tool that comes with the aim to build an environment for quickly creating scalable performant machine learning applications.

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6. CUDA-Convnet

CUDA convent is a tool that is automatically exported from code.google.com/p/cuda-convnet2.

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7. H2O

0xdata’s H2O is an AST statistical, machine learning and math runtime for bigdata that is extensible and users can build blocks using simple math legos in the core.

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8. GoLearn

GoLearn is a batteries included machine learning library for Go. It is well known for simplicity, and easy customization.

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9. Shogun

Shogun is a machine learning toolbox that provides a wide range of unified and efficient Machine Learning methods. This toolbox enables users to easily combine multiple data representations, algorithm classes, and general purpose tools. Shogun is written in C++, Its SWIG library makes promises to expand the horizon and doesn’t limit it to C++ but also in Java, Python, C#, Ruby, R, Lua, Octave, and Matlab.

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10. Cloudera Oryx

Cloudera Oryx is a simple real-time large-scale machine learning infrastructure. It is an open source project provides simple, real-time large-scale machine learning or predictive analytics infrastructure.

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11. Weka

Weka is a collection of machine learning algorithms for data mining. The algorithms can be applied directly to a dataset or called from your own Java code. It comprises of tools for data pre-processing, classification, regression, clustering, association rules and visualization.

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12. MLlib

MLlib is a tool that is usable in Java, Scala and Python. It fits into Spark’s APIs and interoperates with NumPy in Python.

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To wrap up, these above listed 12 best machine learning tools will help you all increase your productivity. Besides smart functionality for individual apps or whole frameworks, these machine learning tools are quite easy to use.

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