Are you interested to learn what lays behind the most advanced technologies? 

Our smartphones use intelligent techniques to adjust the brightness of the screen to the environment. Instagram and Facebook offer us the most interesting materials based on what we have already read in the past.

If you have a business, you know who AI-powered software helps to analyze the customers and offer the best promotions based on many factors such as their gender, age, geographical location, interests, etc. 

You can learn about the machine learning techniques that lay behind your experience. In this post, we will talk about 5 simplest ML techniques that are easy to understand even if you know nothing about machine learning.

Machine learning techniques easily explained

A variety of different techniques exist because different methods are used to solve various problems. 


Classification is a machine learning technique devoted to putting an object into a number of groups based on their similarities. There are many objects (or situations), divided into classes according to some rule. There is a set of objects and it is known what classes they belong to. This set is called a training sample. The class of the remaining objects is not known. It is required to construct an algorithm capable of classifying an arbitrary object from the original set. 


Many people confuse clustering with classification. However, in clustering, the task of an algorithm is to find hidden patterns and place the object into the groups. The multitude of clusters is unknown to the researcher and neither is he familiar with the features that allow assigning this or that label to an object.
While classification is an example of supervised learning (which means a machine needs a teacher), clustering belongs to unsupervised machine learning techniques. 


Regression helps to model the relationship between one independent input variable and an output dependent variable. It only works if there is a linear dependency between the two. Regression helps to make accurate predictions based on the presented data. For example, you can try to forecast how well a house will sell for a certain price in the range of $ 100,000 to $ 200,000.

Ensemble methods

The ensemble is a method when you combine a couple of different algorithms to solve the same problem. For example, bagging and boosting are examples of ensemble methods.

Sometimes the results that basic algorithms provide are inaccurate and you want to improve their performance. Combining multiple models at once and choosing the average result, you manage to solve the problem much more efficiently.

Neural networks

An artificial neural network is made up of many elements called neurons connected with each other into layers. They work similar to a biological neural network that is made of nerve cells. Imitating the work of the human brain, NN acts not only according to a strict algorithm and formulas but also accumulates past experience and uses it to learn.

Summing up

Machine learning is the technology that everybody uses today. If you learn to understand it, it will be much easier for you to keep up with the latest innovations and use their potential to the fullest.