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  2. Information gain (decision tree) - Wikipedia

    en.wikipedia.org/wiki/Information_gain_(decision...

    In machine learning, this concept can be used to define a preferred sequence of attributes to investigate to most rapidly narrow down the state of X. Such a sequence (which depends on the outcome of the investigation of previous attributes at each stage) is called a decision tree , and when applied in the area of machine learning is known as ...

  3. SWAYAM - Wikipedia

    en.wikipedia.org/wiki/SWAYAM

    SWAYAM has accumulated 203 partnering institutes, 2,748 completed courses, 12,541,992 student enrollments, 915,538 exam registrations, and 654,664 successful certificates. [7] SWAYAM (meaning 'Self' in Sanskrit) [8] is an acronym that stands for "Study Webs of Active-Learning for Young Aspiring Minds" [9]

  4. Self-organizing map - Wikipedia

    en.wikipedia.org/wiki/Self-organizing_map

    A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher-dimensional data set while preserving the topological structure of the data.

  5. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    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]

  6. National Programme on Technology Enhanced Learning

    en.wikipedia.org/wiki/National_Programme_on...

    The National Programme on Technology Enhanced Learning (NPTEL) is an Indian e-learning platform for university-level science, technology, engineering, and mathematics (STEM) subjects. NPTEL is the largest e-repository in the world of courses in engineering, basic sciences and selected humanities and management subjects. [ 1 ]

  7. Timeline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Timeline_of_machine_learning

    Pioneering machine learning research is conducted using simple algorithms. 1960s: 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