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scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
AIMA gives detailed information about the working of algorithms in AI. The book's chapters span from classical AI topics like searching algorithms and first-order logic, propositional logic and probabilistic reasoning to advanced topics such as multi-agent systems, constraint satisfaction problems, optimization problems, artificial neural networks, deep learning, reinforcement learning, and ...
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]
OpenCog, a GPL-licensed framework for artificial intelligence written in C++, Python and Scheme. [15] PolyAnalyst: A commercial tool for data mining, text mining, and knowledge management. [89] RapidMiner, an environment for machine learning and data mining, now developed commercially. [90]
Today LEARN as the National Research and Education Network in Sri Lanka, connects all of the UGC funded Sri Lankan national universities, a number of public universities, higher education institutes under other ministries, the University Grants Commission, the Ministry of Higher Education, and six national research institutions. [7]
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
Extreme learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with a single layer or multiple layers of hidden nodes, where the parameters of hidden nodes (not just the weights connecting inputs to hidden nodes) need to be tuned.
The magazine said that the book was not easy to read, but that it would expose experienced programmers to both old and new topics. [ 8 ] A review of SICP as an undergraduate textbook by Philip Wadler noted the weaknesses of the Scheme language as an introductory language for a computer science course. [ 9 ]
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