![]() For example, if you would like to use Association Rule Mining as a training model, you have to dissociate numeric and continuous attributes. The pre-process stage is named as Filter in Weka, you can click the ‘Choose’ button from Filter and apply any filter you want. If you could follow all the steps so far, you can load your data set successfully and you’ll see attribute names (it is illustrated at the red area on above images). It isn’t that simple of course, but from a basic perspective we can explain like above. If your data are mixed or, for example, if you’re going to train a robot, then Reinforcement Learning is right to use. If your data is unlabeled but has a certain characteristic then you can use clustering which is also known as Unsupervised Learning. Basically, if you have labeled data it is good to use the classification method which also known as Supervised Learning. Depending on the data type, the technique is decided. Machine learning can be divided into 3 main topics in terms of learning: Classification, Clustering and Reinforcement Learning. In a nutshell, when it comes to Artificial Intelligence, every move and every result comes from a calculation for today’s technology! ![]() ![]() But in fact, the whole concept is old and unless you give a soul and consciousness to a robot it is not possible for them to conquer the world. People mostly think that AI is dangerous because robots might become so intelligent they take over the world. The concepts of “Artificial Intelligence”, “Deep Learning” and “Machine Learning” are getting so popular among in every society nowadays. ![]()
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January 2023
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