Types of Machine Learning

Machine learning is how our computer or an algorithm learns and gives better performance after a certain period of time.

Learning of an algorithm is largely divided into three categories:

Supervised Learning: In this type of learning we provide certain training data to an algorithm which is a mapping between an input and output. The algorithm learns based on the given data and then predicts the result for different scenarios in the future. Regression and classification are different types of supervised learning. Regression is a type of learning used to predict the values in different scenarios whereas classification is used to classify the data to given groups or classes.

Applications:

Unsupervised Learning: This type of learning also requires data for an algorithm to be trained only difference is that there is no mapping between an input and output. The algorithm generates various types of clusters based on given data and then predicts the cluster to which the data belongs. Clusters are not title and are not defined by humans. Clusters are formed based on the similarity between the inputs. K-means clustering is one of the famous algorithms of this type of learning.

Applications:

Reinforcement Learning: The difference between this type of learning and other types of learning is that this type of learning doesn’t require data as a prerequisite. Even if you don’t have data you can build applications for this type of learning. This type of learning learns from experience. It runs the algorithm on given data process it and generates the result. Then it takes feedback on whether the result was correct or not and then performs a certain action based on the feedback. It can be used for driverless car or for bots to play certain games which will be trained based on experience.

Applications:

We’ll learn about different types of learning and their algorithms in deep in next story.

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