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Artificial Intelligence called to be a part of Computer Science focuses majorly on intelligent machines creation that has got the capability of reacting with the humans. Machines are able to gain new experiences, can easily adjust with the new inputs and perform human like tasks. All this is made possible due to Artificial Intelligence.
When Philosophers were trying to make sense of human thinking in respect to the system, they turned out to invent the concept Artificial Intelligence in the year 1956. Philosophy is called to play a significant role in the introduction and growth of Artificial Intelligence. AI is applicable in various areas like:
Expert System is the area where the computers are created and programmed in a way that enables them to take decisions in real life situation.
Robotics, quite popular as computer programmed without the ability of seeing, hearing and reacting to the sensory stimuli including light, heat, sound, temperature and pressure.
Natural Language where the chatbots are developed in a way that they can easily identify natural human language if there is a direct communication with a customer.
Gaming Systems makes the manipulation of strategic games possible.
Neural System reproduces various types of physical connections that occur in the human brains. All this is being done by simulating intelligence.
Machine learning is a part of Artificial Intelligence. It helps in accurate application of the Artificial Intelligence. All this makes the system capable enough to automatically learn and improve just from the experience without the need of getting programmed. Machine learning constantly strives to develop the computer programs in a way that they are able to access data and can use it to learn for their own use.
The process of machine learning involves two techniques which are Supervised and Unsupervised Learning. Supervised Model has an objective of predicting the future outputs for which it uses the known input and output to train a model. On the other hand, Unsupervised Learning focuses more on identifying hidden patters or the intrinsic structures in the input data.
Supervised Machine Learning develops a model which is predictive models and is being prepared for both input and output data. To develop predictive models, Supervised Learning uses classification and regression techniques.
Unsupervised Learning uses only group and data which are based on input data. The most common technique of unsupervised learning is Clustering.
Machine Learning is useful in many aspects in our day to day life which are as follows: