Intelligent machines are not a new topic anymore, as new technological advances have made it a reality. Today one of the best thing that is happening all across the industries is that the machines are self-learning and have become highly intelligent in every way. Technologies like personal assistant, virtual doctors, recommendations on shopping websites, mobile phones with automation, etc. are all machine learning examples. So the general question that pops up is, how do these machines know?
About machine learning
Well the simplest way of machine learning to be explained is that machines start acting based on their experience and past actions. In ML technology, computers are enabled to think and decide based on the past behavior of the user. Therefore, one can say machines are programmed in such a way that they can decide and act based on the data accumulated from their experience. Machine learning involves mathematical modeling which involves iterative decision making and data analysis. The way the machines behave can be different in ML techniques, thus there are three types of machine learning. The types of machine learning are explained further:
Supervised learning is as simple as teaching and training a machine to act accordingly. It is also the easiest to implement and most popular among the three when it comes to machine learning. In this type of learning, the data examples are fed in the form of an algorithm that can be labeled pair by pair manner. To check the answer, predictions are done by the machine and the feedback is given according to the right answer or wrong answer. This leads to proper conditioning, and the machine learns to deduce the relationship between the variables. After a certain period, the machines can learn even to understand the never seen before cases. Some of the common supervised learning examples are voice recognition, facial recognition, spam selection and classification, etc.
In unsupervised learning, there are no labels that are fed into the machine algorithm. Instead, huge amounts of data are fed into the machines which can be understood and derived from the machine itself with the help of the properties and tools provided. The machines are also capable of grouping and clustering the data fed into the machine, which can become understandable by humans with much ease. With unsupervised learning, the scope of handling unlabeled data that is produced everyday from everywhere becomes easier and near to reality. This type of learning can boost businesses and can also help in increasing the productivity. Some of the examples are like grouping the user logs, recommendation systems, customer behavior tracking, etc.
Reinforcement learning is a lot different from the other two mentioned above. In this the labels are not insulated in any way. Instead, the machines are left to learn from their mistakes. In this, the reinforcement algorithm is used for a machine and environment and then is left to make several mistakes. However, one will have to provide them with proper indications where better decisions and bad decisions can be separated from each other. This way the machines learn gradually and start making lesser mistakes and take more decisive paths in the right direction. Some of the examples of this type of learning are industrial simulation, video games, resource management, etc.
Machine learning courses is overall a part of artificial intelligence and thus can be regarded as the future of technology. Therefore, this is the right time to delve into the field which carries a significant amount of future scope and chances to have better career.
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