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Types and different Learning criteria of Machine Learning

Machine learning has become a trend, almost everyone is familiar with these magical technologies of machine learning, artifice technologies, and augmented reality.

Machine learning has different types, have their different way of working and learning criteria. Generally, there are three main types, if you are an AI engineer or a data scientist you must be familiar with these three basic types of machine learning. In this article, you will get knowledge about these types of machine learning and their approaches and differences.

The Technology of machine learning has three main types.

  • Supervised Learning (I want a teacher)
  • Unsupervised learning (I can learn automatically)
  • Reinforcement Learning (I have my own rules and way of learning)

Supervised Machine Learning

Supervised learning has to be guided with a dataset, this type of machine learning needs a teacher or tutor. A teacher is needed to guide and train its model and make it enable to work, think and make a decision. Supervised machine learning needs supervision and training. once supervised learning gets proper knowledge and becomes well trained, it can predict and start making a decision. The supervised machine can work accurately and efficiently according to the newly given data.

Unsupervised Machine Learning

Unsupervised machine learning did not need a teacher, it is a self-sufficient type of machine learning. It works automatically, once the dataset is given to an unsupervised model in can observe and learn through the dataset. An unsupervised model or machine can create a cluster and finds relationships and patterns automatically in the dataset.

It is a powerful type of machine learning which can work automatically and perfectly. But there are some tasks that it difficult for unsupervised machine learning like it cannot add labels to its clusters. For example, it cannot give a label to a group of apples and bananas, but it will separate all bananas from the apples.

Unsupervised machine learning creates different clusters and puts the dataset into these clusters like if we give it images of apples and bananas, it will create clusters and put those data of images into the clusters. Whenever we give it new data, it put the new data into the cluster. The unsupervised model creates clusters after building some relationships and patterns according to the given data.

Reinforcement Machine Learning

This type of machine learnings follow the rule of hit and trial, It works smartly as s agent, it can be trained itself after interacting with environmental factors and finding out the best working factors, Reinforcement model finds a good outcome for its system. The agent of reinforcement learning is also gets rewarded when it works accurately and get penalize when it did not work correctly or give wrong answers. When the model gets positive reviews or remarks, it can train its model itself and get ready for making new predictions and decisions according to the data stored in it.

Now, you must be fully aware of the three basic types of machine learning. Machine learning still needs to be more advanced, new ways of machine learning can be expected in the future. I hope this tutorial of machine learning will be helpful for you.

 

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