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Most Important Machine Learning Tools

Most Important Machine Learning Tools

Machine learning tools can be categorized into platforms or libraries. A platform provides all the tools needed to implement a project, while a library provides only certain functionality. However, some machine learning platforms are also libraries. Machine learning tools can also be distinguished by their interface. Some tools provide a graphical user interface, some a

Machine learning tools can be categorized into platforms or libraries. A platform provides all the tools needed to implement a project, while a library provides only certain functionality.

However, some machine learning platforms are also libraries.

Machine learning tools can also be distinguished by their interface. Some tools provide a graphical user interface, some a command-line interface, and others an application programming interface (IPA).

Another way to distinguish machine learning tools is to determine whether a tool is installed on a local machine, or remotely on a third-party server in the cloud.

Here is a non-exhaustive list of important machine learning tools:

1. Free and open-source tools

PyTorch

PyTorch is an open-source deep learning framework built with the stability and support needed for production deployment to be flexible and modular for research. A Python package for high-level features is provided by PyTorch.

Scikit-learn

Scikit-learn is a free library for the Python programming language. It offers various classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, and k-means. It is designed to interact with the digital and scientific libraries of Python, NumPy, and SciPy.

Apache Hadoop

Hadoop is an open-source processing framework that manages data processing and storage for big data applications running in cluster systems. It is at the heart of a growing system of big data technologies primarily used for advanced analytics initiatives, including predictive analytics, data mining, and machine learning applications.

Hadoop can process various forms of structured and unstructured data, giving users more flexibility in data collection, processing, and analysis than relational databases and data warehouses.

Apache Spark

Spark is a general-purpose data processing engine suitable for a wide range of circumstances. In addition to the Spark data processing engine, Apache Spark provides libraries for SQL language, machine learning, graphical computation, and stream processing, which can be used together in an application.

Microsoft’s Cognitive Toolkit (CNTK)

Previously known as CNTK is a deep learning framework developed by Microsoft Research. She describes neural networks as a series of computational steps through a directed graph.

H2O

H2O is an open-source big data analysis software produced by the company H2O.ai. H2O allows thousands of potential models to be used to discover data patterns.

Orange

Orange is a free toolkit for visualization, machine learning, and data mining. It features a visual programming interface for exploratory data analysis and interactive data visualization. It can also be used as a Python library.

Waikato Environment for Knowledge Analysis (WEKA)

WEKA is a machine learning software suite written in Java, developed at the University of Waikato, in New Zealand. This is free software under the GPL (General Public License).

Massive Online Analysis (MOA)

MOA is a free open source data stream mining related project written in Java and developed at the University of Waikato, New Zealand.

TensorFlow

TensorFlow is a machine learning library, it is a toolkit for solving extremely complex mathematical problems with ease. It allows researchers to develop experimental learning architectures and turn them into the software.

2. Proprietary tools with free and open editions

KNIME (Konstanz Information Miner)

KNIME is a free and open-source data analysis, reporting, and integration platform. It integrates various elements of machine learning and data mining through a modular data pipeline concept.
A graphical user interface as well as the use of JDBC (Java Database Connectivity) allows the assembly of nodes mixing different data sources, including preprocessing (ETC: extraction, processing, and loading), for modeling as well as analysis and data visualization, with minimal programming or without programming.

RapidMiner

RapidMiner is a data science software platform developed by the company of the same name. It provides an integrated environment for data preparation, machine learning, deep learning, text mining, and predictive analytics.

RapidMiner is used for commercial applications as well as for research, education, training, rapid prototyping, and application development. It supports all stages of the machine learning process, that is, data preparation results visualization, and model validation and optimization.

3. Exclusive proprietary tools

Amazon Web Services (AWS)

AWS is an Amazon subsidiary that provides on-demand cloud computing platforms to individuals, businesses, and governments for a paid subscription. This technology allows subscribers to have a group of virtual machines available at all times over the Internet.

In 2017, AWS comprised over 90 diverse services including IT, storage, networking, databases, analytics, application services, deployment, management, mobility, and more. development tools and tools for the Internet of Things.

IBM SPSS Modeler

IBM SPSS Modeler is data mining and text analysis software developed by IBM. It is used to build predictive models and perform other analytical tasks. IBM SPSS Modeler has a visual interface for using statistical and data mining algorithms without programming.

Wolfram Mathematica

Wolfram Mathematica is a modern technical computing system covering most areas of technical computing, including neural networks, machine learning, image processing, geometry, data science, visualization, etc.

MATLAB (Matrix Laboratory)

MATLAB is a multi-paradigm digital computing environment and proprietary programming language developed by MathWorks. It enables matrix manipulation, function and data plotting, implementation of algorithms, creation of user interfaces as well as interfacing with programs written in other programming languages. Although MATLAB is primarily intended for digital computing, there are optional toolkits for developing machine learning applications.

Microsoft Azure

Microsoft Azure is a cloud computing service created by Microsoft for the creation, testing, deployment, and management of applications and services through data centers managed by Microsoft. It provides software as a service, platform as a service, and infrastructure as a service. Microsoft Azure supports many programming languages, tools, and frameworks, including Microsoft and third-party software and systems.

SAS (Statistical Analysis System)

SAS is a software suite developed by the SAS Institute for advanced data analysis, multivariate analysis, business intelligence, data management, machine learning, and predictive analytics.

Statistica

Statistica is an advanced analysis software package originally developed by StatSoft, for data analysis, data management, statistics, data mining, machine learning, text analysis, and data visualization.

There are, in addition to the tools presented above, a large number of machine learning tools, varying in sophistication and functionality. For people without extensive programming and statistical knowledge, Orange and Weka offer a good balance between ease of use and functionality.

XL Miner, an Excel add-in

Xlminer is a good option for people with a basic knowledge of statistics. This option generally works best with a small data set, however, as very large data sets can crash Excel.

Microsoft has also improved its business intelligence tool called Power BI to incorporate automated artificial intelligence and machine learning without requiring code. The Tableau business intelligence tool has similar functionality.

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