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What Is Machine Learning?

Machine learning is the part of artificial intelligence which helps to find out solutions for problems by identifying the pattern of data base. Mainly it is a domain of computer science. This phenomena mains helps in IT system to solve complex tasks. We can also justify this word as the pattern which runs on the basis of existing algorithm and data sets to decipher adequate solutions. Mainly this machine learning depends upon the past practice. To run this software effectively by generating independent movements the previous actions of individuals is needed. So the suitable algorithm and data base is loaded to the machine previously to perform the required action. To carry out the task some steps has to be followed in machine learning they are

  • Picking out the applicable data by extracting it

  • The overall result must be estimated by calculating the probabilities

  • Following some modifications according to the need

  • Finally moving on to the optimization of the whole project by analysing the patterns

How Machine Learning works?

Machine learning can be compared with the human intelligence. For example if we show a specific object to a child by stating its overall information which made him learn similarly it happens in the machine learning. The specific data output and some commands has to be feed before which helps the computer to learn the specific function. The overall It world is provided by training this technology to develop the project.

Types Of Machine Learning

There are three types of machine learning
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Supervised learning

Providing adequate data to the system with about a program and the program to result. The final result is called as labelled data. In generally it means making a machine to learn the needed data.

Unsupervised learning

In such techniques the system analyzes the own pattern which is not visible to human eye and it works on the lack of label training. Hence this is much beneficial and stress free work to the user.

Reinforcement learning:

This is similar to the human learning. The algorithm and the data is based on the environment and extraction depends upon the onset of actions.

Examples Of Machines Learning?

  • Virtual personal assistant like siri, alexa, Google which works on the voice over.
  • GPS navigation that predicts the traffic
  • Social media platform that personalizing our news feed
  • Video surveillance by managing multiple cameras

Advantages Of Machine Learning

  • Works on the newest data and patterns
  • No human involvement
  • Improvement by the past experience
  • Carryout multiple actions
  • Numerous applications

Disadvantages Of Machine Learning

  • Demand of immense data
  • Need more time to make learn the computers
  • Interpretation results that is based on algorithm
  • Results in high errors