Machine learning is a portion of artificial intelligence. With, IT systems recognize patterns and laws based on existing databases and algorithms and develop solutions—artificial knowledge generated from experience. The knowledge gained from the data can be generalize and use for new problem solutions or previously unknown data analysis.
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So that the software can acquire independently and find solutions, human action is necessary beforehand. For example, the systems must first supply the data and algorithms relevant for learning. In addition, rules must drawn up for analyzing the database and recognizing the patterns. If the right data is available and rules are defined, machine learning systems can do the following.
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Algorithms play a essential role in machine learning. They are accountable for recognizing patterns and generating solutions and can divided into different learning categories.
While with supervise learning, sample models have to be define and specify in advance to match the information to the algorithms’ model groups. With unsupervised learning, the model groups formed automatically based on independently recognized patterns.
Partly supervised learning is a mixture of both methods. Reinforcement learning based on rewards and punishments. This interaction tells the algorithm how to react to different situations. This way of learning is very similar to human learning.
It has a very wide range of possible uses. In the internet environment. It is used, for example, for the following functions.
Other areas of application of machine learning are image and face recognition, automatic recommendation services or the automatic detection of credit card fraud.
It has also receive an enormous boost thanks to the development in the field of big data technology. Since large amounts of data are available in machine learning and have to be processed efficiently, big data systems form the ideal basis for this type of [learning]. With the help of big data, both structure and unstructure data can be analyze quickly and with relatively little hardware expenditure and fed to the learning algorithms.
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