Just in case you weren't sure, there is a huge revolution happening. The revolution is around using data. Rather than developers writing explicit code to perform some computation, machine learning applications, including supervised learning reinforcement learning and statistical classification applications can use the data to create models. Within these categories there are a number of approaches, including #deeplearning, #artificialneuralnetworks, support #vectormachines, #clusteranalysis, #Bayesian networks and learning classifier systems. These tools create a higher level of abstraction of the data, which, in effect, is learning, as defined by Tom Mitchell (taken from Wikipedia): "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." After learning, these tools can make predictions based on new input data. Rather than create code with sets of rules and conditions to model a problem or a situation, these algorithms utilize only the data to form their own rules and models.
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