What are the different types of machine learning?


Machine learning has brought a huge revolution in today’s world. By using this amazing technology, ordinary computers are being trained to become artificially intelligent. To make the machines acquire a myriad of skills, they are programmed with certain types of machine learning.

SUPERVISED LEARNING-
This type of learning is mainly task-oriented and improves the machine’s ability to give accurate responses. The computer is made to solve certain problems and is supposed to give answers. Those answers are compared to real answers and the computers are given feedback. The computers understand the algorithm and recognize the pattern of analyzing questions and their decision-making improves significantly. The ads we see while browsing through the internet are achieved by supervised learning. The learning algorithm enables the machine to place the ads that are popular and have more chances of being clicked. The inappropriate mail automatically goes to the spam folder as a result of supervised learning of certain algorithms. Whenever we post a picture with our friends on Facebook, it recognizes the faces of our friends and suggests tagging them in the photo. This ability of face recognition is developed through supervised learning.

UNSUPERVISED LEARNING-
In this kind of learning, the machine is fed with huge amounts of unlabeled data. The machine is supposed to organize and label the humongous data in groups and clusters so that essential inference can be made from it. The industries can understand the shopping habits of people by analyzing the data through machines and can increase their productivity.  YouTube recommendations of videos are based on data of the people who share the same interests as us while watching a video and unsupervised learning makes machines find a relationship in data patterns and provides video suggestions according to it. Amazon shows us the products bought by people having the same choices as us and increases its sale. The companies use unsupervised learning to segregate customers and do targeted marketing. It is also used to detect fraud in banking sectors. Nowadays, it is also being used to predict the weather and climate of an area.

REINFORCEMENT LEARNING-
The machine is given rewards or a positive signal for working according to the order given and is punished or given negative signals for disobeying it. The algorithm is modified to accept the rewards and minimize mistakes. The machine tries to find out different actions, performs them, compares the results and learns to do the actions with the most reward points. The machine is made to acquire certain skills like playing a video game. Robotics also uses this learning to make robots efficient at doing tasks better. It is also being used in the field of e-commerce and medicine. Reinforced learning is being used by large companies to execute their trading and maximize their profit. It can be used to make training systems that would provide study materials according to the needs of the students.

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