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Rohith ND
Rohith ND

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Introduction to Machine Learning

Machine learning is an artificial intelligence sub-field. It is a computer science research area that deals with methods to identify and implement systems and algorithms by which a computer can learn, based on the examples given in the input. The goal of machine learning is to teach a computer how to recognise complex patterns and make as intelligent decisions as possible.

Machine learning has benefited every technology user today. Optical character recognition technology transforms text images into movable type. Social media platforms use the facial recognition technology to help users tag and share photos of friends. Machine learning-powered recommendation engines suggest what movies or television shows to watch next based on user preferences.

A machine can understand to solve a problem using any of the three approaches

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning

Supervised Learning

Supervised Learning is distinguished by the use of labelled datasets to train algorithms that accurately classify data or predict outcomes. As input data is fed into the model, the weights are adjusted until the model is properly fitted, which occurs as part of the cross validation process. In supervised learning, the computer is fed examples of inputs labelled with the desired outputs. As a result, supervised learning employs patterns to predict label values .

Unsupervised Learning

Unsupervised learning analyses and clusters unlabeled datasets using machine learning algorithms. Without the need for human intervention, these algorithms uncover hidden patterns or data groupings.Unsupervised learning entails training with unlabeled data and then allowing the model to act on that information without supervision.

Reinforcement Learning

Reinforcement Learning is a feedback-based Machine Learning technique in which an agent learns how to behave in a given environment by performing actions and observing the outcomes of those actions. For each positive action, the agent receives positive feedback; for each negative action, the agent receives negative feedback or a penalty. An artificial intelligence is put in a game-like situation in reinforcement learning. The computer uses trial and error to find a solution to the problem. To make the machine do what the programmer wants, the artificial intelligence is rewarded or punished for the actions it takes. Its objective is to maximise total reward.

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