In the last decade or so Artificial Technology has commendably taken a leap forward. So much so that it has become our daily driver. But how did Artificial Intelligence become a thing? It all happened because of Machine Learning. A system that adapts, learns and rectified automatically. Let’s list the top 5 software that complements machine learning and puts it in the spotlight.
Focused on writing Python and sometimes the initial algorithms in Cython, Scikit Learn is a free machine learning software. It was initially developed by David Cournapeau at Google Summer of Code.
Support vector machines, random decision forests, classification, regression to k-mean and DBSCAN the Scikit Learn provides an array of features. Additionally, it interoperates Numpy and SciPy, Pythons numeric and scientific library.
Scikit Learn is compatible with Linux, Windows, macOS and updated IOS version
One of the most flourishing machine learning software for tabulated and structured data. XGBoost has initially developed Tianqi Chen and owned by DMLC or Distributed Machine Learning Community. It is an application of gradient boosted decision trees.
Interfaces supported by XGBoost are CLI, C++, Python, R, Julia, and Java. The software is concentrating on delivering speed and modular performance. Including Modular, Systems and Algorithm feature.
XGBoost is compatible with Linux, Windows, Ubuntu, macOS, and updated IOS versions.
An open source machine learning software primarily focused on GPUs. Originally developed by Ronan Collobert, Koray Kavukcuoglu, and Clement Farabet. Know for its easy to understand and fast scripting language through LuaLIT and C/ CUDA.
Comes with a number of features. Listed- linear algebra routines, neural network, and energy-based models, a powerful N-dimensional array and more.
The Torch is compatible with Linux, Windows, macOS and updated IOS and Android versions.
Again, an open source machine learning framework, H20 was developed and is owned by a company named H20.ai. Famously known for its usage in cloud-based systems and Apache Hadoop. Written in 3 languages mainly R, Python, and Java, with H2O its all about capacity model works.
Among various other features, H2O can withstand large model analysis. Also, dictates smooth cloud-based computing and uses Iterative methods for faster real time results.
H2O is compatible with Linux, Windows, and macOS.
These were my top 5 picks of software one can use for machine learning. Ultimately it depends on an individual’s need if it’s speed or prolonged data analysis. The software you use will depend on variables such factors. Each has its own specific benefits. Step into the artificial reality and discover new technological advancements with this software.