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  1. Machine learning

    Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence

  2. Active learning (machine learning)

    Active learning is a special case of machine learning in which a learning algorithm can interactively query a user (or some other information source) to

  3. Boosting (machine learning)

    In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine

  4. Deep learning

    Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with

  5. List of datasets for machine-learning research

    for machine-learning research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major

  6. Automated machine learning

    Automated machine learning (AutoML) is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete

  7. Hyperparameter (machine learning)

    In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters

  8. Supervised learning

    Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers

  9. Transformer (machine learning model)

    The Transformer is a deep learning model introduced in 2017, used primarily in the field of natural language processing (NLP). Like recurrent neural networks

  10. Support vector machine

    In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that