People always find hard to understand which machine learning algorithm has to be applied on given problem. Today we have infinite number of problem and we might think that we have infinite number of solution available around it. But when we start classifying the problems in different categories we usually find that these infinite problems can fir in finite categories and infinite solutions change to finite solutions. In this post, will try to explain more on what is an Algorithm and under which circumstances we have to use which type of machine learning algorithm.

To tell a computer what it has to do, in that case you need to write a program. A program is nothing but a set of instructions in the form of some syntax. This syntax can be written in any programming language like Java, C, Python, Ruby on Rails etc. e.g if you have to write a program to print 1 to 20 numbers, in this case you can opt for any kind of programming language. The syntax will be different but the logic will remain be the same. Now the question comes what is Logic? Logic is nothing but it’s an algorithm. An algorithm is a step by stem procedure of solving a problem in computer world.

**Read More about types of Machine Learning**

**How Does Machine Learning Helps To Solve Any Kind Of Problem?**The problem can be divided into different kind of category as mentioned below:- 1.

**Classification Problem**: When we know that any problem is having set number of outputs during that time we can use classification algorithm. E.g. Differentiating between apple and oranges is a type of classification problem.

2.

**Anomaly Detection Problem**: When a same type of input is given to system and if any deviations happen in that input in that case anomaly detection algorithm is used to detect that change. It analyzes a certain type of pattern and alerts whenever there is change in pattern.

3.

**Regression Problem**: Whenever we want machine to give us a number as output during that time we have to apply the regression algorithm. It is used to calculate numeric values. E.g. what is the minimum investment should I put in to become millionaire in 10 years.

4.

**Clustering Problem**: Trying to find what is the structure behind the given data sets during that time clustering algorithm is required. By understanding how data is organized you can better predict the behavior of particular event.

5.

**Reinforcement Problem**: When decision has to be made in that case, reinforcement learning algorithm is applied.

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