Cool and Powerful Agriculture Machines That Are On Another Level Part 2
Machine learning is a sub-branch of computer science that was developed mainly in 1959 from the study of numerical learning and model recognition in artificial intelligence. Machine learning is a system that studies the operation and construction of algorithms that can learn as a structural function and make predictions from data. Such algorithms work by building a model to be able to make data-based predictions and decisions from sample inputs, rather than following static program instructions exactly.
The list of the main concepts of machine learning, together with their description, is as follows:
► Supervised Learning: Data is taken from interactive systems and organized in a certain order
► Unsupervised Learning: It is the study of groups in data that do not contain classroom information
► Semi-Supervised Learning: This concept is exactly between the two concepts above and is the dec of a large amount of unlabeled data and a small amount of labeled data together.
► Reinforced Learning: This is an assessment of the teacher as true or false for the result produced by the system.
► Intensive Learning: Also known as hierarchical learning. This learning method is machine learning developed by a set of algorithms based on model attempts obtained using high-level abstractions in data created from many linear and nonlinear transformations and multiprocessing layers in deep graphs.
Machine learning as a scientific endeavor has historically emerged from the search for artificial intelligence. Some academic studies conducted in the past have shown that machines have to learn data after a certain stage, and thus researchers have conducted their studies to approach the problems that arise on this topic in various symbolic ways. In particular, the probabilistic logic technique was also used in automated medical diagnostic systems.
Machine learning as a separate field began to develop again in the 1990s. The goal of the field change is to capture artificial intelligence in addressing solvable problems in practical life. Machine learning and data mining often use the same methods, and these methods overlap significantly. These methods can be distinguished in general terms as follows:
► Machine learning focuses on predictions made from learned data based on known characteristics.
► Data mining, on the other hand, focuses on the discovery of unknown features in the data (history). This is a step in the analysis of information discovery in databases.
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