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OpenML: [493] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [494] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...
Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession. [4] Data science is "a concept to unify statistics, data analysis, informatics, and their related methods" to "understand and analyze actual phenomena" with data. [5]
Key topics include machine learning, deep learning, natural language processing and computer vision. Many universities now offer specialized programs in AI engineering at both the undergraduate and postgraduate levels, including hands-on labs, project-based learning, and interdisciplinary courses that bridge AI theory with engineering practices.
Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases).
ML.NET is a free software machine learning library for the C# and F# programming languages. [4] [5] [6] It also supports Python models when used together with NimbusML.The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. [7]
Orange: A component-based data mining and machine learning software suite written in the Python language. PSPP: Data mining and statistics software under the GNU Project similar to SPSS; R: A programming language and software environment for statistical computing, data mining, and graphics. It is part of the GNU Project. scikit-learn: An open ...
Python: scikit-learn, a package for machine learning in Python offers packages for ensemble learning including packages for bagging, voting and averaging methods. MATLAB : classification ensembles are implemented in Statistics and Machine Learning Toolbox.
Hands-On Data Visualization details design methods for interactive charts and customized maps, starting with drag-and-drop tools including Google Sheets, Datawrapper, and Tableau Public. It progresses to detail methods for editing source code templates built with Chart.js, Highcharts, and Leaflet on GitHub.