Question

supervised learning algorithm and unsupervised learning algorithm step by step process.
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Answer 1

Answer:

An algorithm is a set of instructions for solving a problem or accomplishing a task. One common example of an algorithm is a recipe, which consists of specific instructions for preparing a dish or meal. Every computerized device uses algorithms to perform its functions.

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Answer 2

Answer:

Supervised Machine Learning

The majority of practical machine learning uses supervised learning.  

Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.  

                                                 Y = f(X)  

The goal is to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.

 

It is called supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. We know the correct answers, the algorithm iteratively makes predictions on the training data and is corrected by the teacher. Learning stops when the algorithm achieves an acceptable level of performance.

Some popular examples of supervised machine learning algorithms are:  

• Linear regression for regression problems.

• Random forest for classification and regression problems.

• Support vector machines for classification problems.

Unsupervised Machine Learning

Unsupervised learning is where you only have input data (X) and no corresponding output variables.  

The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.

 

These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.

Some popular examples of unsupervised learning algorithms are:  

• k-means for clustering problems.

• Apriori algorithm for association rule learning problems.

Summary:

• Supervised: All data is labeled and the algorithms learn to predict the output from the input data.

• Unsupervised: All data is unlabeled and the algorithms learn to inherent structure from the input data.

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