Machine Learning is a subset of artificial intelligence, thanks to which we can have systems that learn and develop automatically and without explicit planning.

Artificial intelligence, The question may be whether computers can learn from the data provided to them. We have to say yes. Computers can receive new data in new situations and adapt to it, learn from previous calculations, and make repeatable decisions.

In the following, we are going to talk a little more about machine learning or machine learning.

What is machine learning?

Machine Learning is a subset of artificial intelligence, thanks to which we can have systems that learn and develop automatically and without explicit planning. Machine learning essentially focuses on the development of computer programs that can access and learn data on their own.

The learning process begins with observations or data such as examples, direct experiences, structures, etc., and in this data we seek to find patterns that help us make better decisions in the future. The main purpose of machine learning is to allow computers to automatically learn and set actions without human intervention or assistance.

 

Classification of machine learning methods

Learning machine with supervisor

In this category, the system can apply what it has already learned to the future and predict future events by tagging data and samples. In fact, the system starts with analyzing known data and eventually generates a learning algorithm and inferred function to predict output values. Even this algorithm can compare its outputs with the correct ones to find out how accurate its operation is and get its error rate.

 

Unsupervised learning machine

In this way, our data is not categorized and tagged. Unsupervised machine learning studies how the system can infer a function that can find hidden structures from unlabeled data. However, these algorithms cannot detect the correct output.

 

Semi-supervised machine learning

These machine learning algorithms are something between observer and non-observer modes that use both labeled and unlabeled data to learn. Usually in this method, a small amount of data is labeled and a large amount of it is unlabeled. Systems that use this method can significantly increase the accuracy of learning. This method is usually used in situations where obtaining labeled data requires relevant skills and resources.

 

Reinforcement machine learning

This method is a machine learning method that uses trial and error to strengthen and improve its performance. In fact, in this method, the system communicates with the environment and uses the feedback it receives to improve itself. In this way, the machine or software automatically finds the ideal behavior to maximize its performance. Then in fact, these feedbacks are reinforcing signals.

 

Machine learning applications

Machine learning in various fields such as weather forecasting, medical diagnostics, data analysis, surveillance of CCTV and network cameras, use in social networks (for personalizing news feeds, targeted advertising, etc.), filtering malware and … is applicable.

 

In the following articles we will decide to talk about machine learning techniques.