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e. In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning techniques which generate the best predictor by learning on the entire training data set at once.
A massive open online course ( MOOC / muːk /) or an open online course is an online course aimed at unlimited participation and open access via the Web. [1] In addition to traditional course materials, such as filmed lectures, readings, and problem sets, many MOOCs provide interactive courses with user forums or social media discussions to ...
In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets. The model is initially fit on a training data set, [3] which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. [4]
Rapid learning. Rapid learning (or Rapid eLearning Development) has traditionally referred to a methodology to build e-learning courses rapidly. [1] Typically the author will create slides in PowerPoint, record audio and video narration on top of the slides, and then use software to add tests, or even collaboration activities between the slides.
The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by ...
Supervised learning. Supervised learning ( SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. [1]
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