In the last post, we have discussed Machine learning. Now in this post, we will discuss some more detail about algorithms and trust me this is one of the most important objects which a Machine learning engineer should know.

These objects are nothing but the algorithms. As a data scientist or Machine learning engineer, the first and most important thing are we should know

what is the data ?

What the result is ?

what analysis do you need to apply to get the desired result or prediction?

I know this is pretty much clear but let me explain with an example. Suppose, we have students data with high school ‘s internal assignments and we need to predict if the student can be pass in the final exam or not.

Now let me give you a brief overview of some of the algorithm types which we may require in Azure Machine Learning. Although, there are much more types ,subtypes available but will not go in deep. So, let’s start

**1) Two- Class Algorithm Type:-**

We will apply this algorithm type when the prediction result in either Yes/No or true/false or 1/0. for example, a student can be pass or not.

**2) Classification Algorithm Type:- **

This is another algorithm type which help us to predict answer like which Kabaddi team or cricket you will cheer or which political team you will vote.

**3) Linear Regression Algorithm Type:-**

This is one of the common prediction methods which everyone applies Smile sometimes. for example, in office, you can predict an engineer’s salary range depending upon last few engineer’s salary, prediction of property selling amount range Like this plot might be from 20 lac- 25 lac depending on last few years property price.

**4) Anomaly detection Algorithm Type:- **

By the name, it is clear we need to find anomalies. for example, you have to determine from a group of white cows and black cow you need to find out odd color cow means black color cow.

I hope the all the above algorithm types is clear. In next post, we actually do the step by step Microsoft Azure Learning so don’t worry about that.

Please, provide your inputs

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