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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]
C# (/ ˌ s iː ˈ ʃ ɑːr p / see SHARP) [b] is a general-purpose high-level programming language supporting multiple paradigms. C# encompasses static typing, [ 16 ] : 4 strong typing , lexically scoped , imperative , declarative , functional , generic , [ 16 ] : 22 object-oriented ( class -based), and component-oriented programming disciplines.
C# can be used to develop high level machine learning models using Microsoft’s .NET suite. ML.NET was developed to aid integration with existing .NET projects, simplifying the process for existing software using the .NET platform. Smalltalk has been used extensively for simulations, neural networks, machine learning, and genetic algorithms.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Bayesian methods are introduced for probabilistic inference in machine learning. [1] 1970s 'AI winter' caused by pessimism about machine learning effectiveness. 1980s: Rediscovery of backpropagation causes a resurgence in machine learning research. 1990s: Work on Machine learning shifts from a knowledge-driven approach to a data-driven approach.
ML.NET is a free software machine learning library for the C# programming language. [3] [4] The NAG Library has C# API. Commercially licensed. NMath by CenterSpace Software: Commercial numerical component libraries for the .NET platform, including signal processing (FFT) classes, a linear algebra (LAPACK & BLAS) framework, and a statistics package.
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm.The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier.
McCaffrey, J.D., "Machine Learning Using C# Succinctly for Syncfusion", [7] In Machine Learning Using C# Succinctly, you'll learn several different approaches to applying machine learning to data analysis and prediction problems. October 2014.