Problems solved by using machine learning
In machine learning, all the issues are facing it categorized into three categories that are-
Every problem facing in machine learning can be resolve using in one of these three categories.
In a regression type of problem, the output always continues quantity. A continuous amount is any quantity that can have an infinite range of values, example age, salary, price, etc. It is supervised learning and solved by using supervised learning algorithms like linear regression.
In the classification type of problem, the output is always categorical value. It computes the category of the data. A Categorical is a value like the gender of a person. Male or female, veg or non-veg, green of blue. These type of problem can be resolve by using supervised learning classification algorithms, like support vector machine, naive Bayes, logistic regression, K nearest neighbour, etc.
In clustering type of problem, assigned the input into two or more clusters based on feature similarity. In this we don’t have much more input information, so we have to find patterns and understand that data point which are similar to clustered into one group, and which are different of one group are clustered into another group, example Netflix. It clusters their user into similar groups based on their interest, based on their age, and so on. This can be done by using an unsupervised learning algorithm like K-means.
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