What is Machine Learning?

If we want to define Machine Learning in simple terms, we should say that Machine Learning is the science that teaches machines how to learn new things on their own. After hearing this sentence, you are probably asking yourself why machines should learn from themselves. What is the benefit of this work for us? Let's examine this sentence with an example.

Suppose we want to clean the floor. When a human does this work, the quality of the work can vary greatly because it depends on various factors. The probability that a human will get sick or get tired after a few hours of work, or even want to give up, is very high.

But if we teach a machine to recognize the dirt on the floor and start cleaning its surface based on the amount of dirt and the condition of the floor. If we define this task for a machine, it can do it much better than a human. Without getting tired or having the possibility of getting sick. The machine in question should be able to answer the following questions:

When does the Earth need to be cleaned?

How long should the cleaning of the earth continue?

Etc.

This is what machine learning does. It allows the machine to learn from itself and constantly improve its behavior.

Machines do not have a brain and the power to think like us humans, so there must be a way to teach them to think, and this is where machine learning models can come to our aid. In this way, the machine receives data from the external environment and delivers it to the relevant model. Then this model makes decisions based on the existing conditions. In the example of cleaning the ground, the machine can obtain various information with the data it receives and delivers to the model:

When the ground needs to be cleaned or when it is clean

How long should this cleaning continue

Etc.

Machine learning is a popular subfield of artificial intelligence that helps machines or computers learn new things

Difference between data mining and machine learning

Data mining was introduced in 1930, and its goal is to find useful, hidden,n and valid information from a huge amount of data. Machine learning, however, was introduced in 1950 and involves applying a model extracted from training data to new data. Both of these techniques have something in common in terms of trying to find useful data, but they are different in terms of responsibility, origin, implementation, nature, use cases, and techniques used.

Data mining tries to extract meaningful rules and relationships from data, but machine learning tries to teach the computer the extracted rules. While applying data mining techniques, we can develop our model. The main difference between data mining and machine learning is that in data mining, information extraction is not possible without human intervention, but in machine learning, human presence is up to the stage of selecting and applying the machine learning algorithm, and after that, its results are used once and for all. The results obtained from machine learning have higher accuracy than those from data mining.