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Machine learning involves computers deriving meaning and making predictions from things like language, intentions and behavior.
In relation to context-aware applications, machine learning involves a wide variety of algorithms which can be classified into different categories based on the following methods [19]: Supervised and unsupervised learning (explained below).
A subset of artificial intelligence, machine learning involves creating powerful algorithms that spot patterns and relationships from historical data, and get better over time at making predictions about brand new data sets based on their experience.
Machine learning involves computer programs that use experience gained from exploration of a dataset to improve performance or predictive ability.
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It was a very basic experience (especially when you can see through the coding logic) and it failed to convince me that there was a use of AI or machine learning involved in the process.
Machine learning (ML) involves building models from labeled training dataset (instances of texts or sentences) in order to determine the orientation of a document.
We developed our technology based on deep learning, an approach to statistical machine learning that involves stacking simple projections to form powerful hierarchical models of a signal.
It works thanks to machine learning, which involves having Cortana scan your emails in order to generate its timely suggestions of tasks – something it does only with users' permission, of course.
The first step of machine learning classification involves selecting the best algorithm most suited to classifying the datasets.
In machine learning, modeling involves classification or prediction that associates patterns from data points with classes that express different concepts.
Adapting the tree-based method to the probabilistic setting is a novel challenge in machine learning and involves devising a novel decision function for learning the tree.
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