What is the difference between Supervised learning, Unsupervised learning and Reinforcement learning?

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Machine Learning

Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead.

 

Building a model by learning the patterns of historical data with some relationship between data to make a data-driven prediction.

 

Types of Machine Learning

 

• Supervised Learning

 

• Unsupervised Learning

 

• Reinforcement Learning

 

 

Supervised learning

In a supervised learning model, the algorithm learns on a labeled dataset, to generate reasonable

predictions for the response to new data. (Forecasting outcome of new data)

 

• Regression

 

• Classification

 

Unsupervised learning

An unsupervised model, in contrast, provides unlabelled data that the algorithm tries to make sense of by extracting features, co-occurrence and underlying patterns on its own. We use unsupervised learning for

 

• Clustering

 

• Anomaly detection

 

• Association

 

• Autoencoders

 

Reinforcement Learning

Reinforcement learning is less supervised and depends on the learning agent in determining the output solutions by arriving at different possible ways to achieve the best possible solution.

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