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What is machine learning? What is its importance and applications?

Study guide




We know that humans and other living things are teachable. But what about robots and computers? Can we train these creatures made of metal, plastic and silicon? The answer is yes! Machine learning is a branch of artificial intelligence that aims to build robots and design computer programs that can learn and act on what they have learned without human intervention. In this article, we will talk about machine learning in simple language, its importance, its applications in various fields, and the prerequisites for entering this field. Stay with us until the end of the article.

What is machine learning or machine learning?

Arthur Samuel, a pioneer in the field of artificial intelligence and computer games, first used the term in 1959. He defined machine learning as the ability of a computer to learn without the need for programming.

Machine learning is the process by which a robot or computer acquires the ability to learn. Like humans who learn through training and experience, we can teach robots to learn. Machine learning is actually a branch of artificial intelligence. Its purpose is to design algorithms and programs that help the robot learn from the data or experiences gained.

The machine learning process occurs by examining data, experimenting, or using pre-given commands, and the goal is that in the future, the robot will be able to make decisions in similar situations and behave appropriately for that situation, like we teach a child the alphabet to Be able to make words in the future. We want to teach robots an alphabet that they can put together to make meaningful sentences. Of course, this is just a handy example to better understand the concept, otherwise the trouble of teaching robots is much more than the trouble of teaching a human child.

To better understand machine learning, consider these examples:

  • Google’s car is an example of the application of machine learning.
  • Online shopping offers or newsletters that are displayed to you under the Google search engine are also under the control of a machine that follows your behavior in cyberspace and is somewhat familiar with your taste.
  • Want to know what customers are saying about you in cyberspace? Machine learning helps you.
  • Detection of fraud and fraud is also one of the applications of machine learning.

The ultimate goal of machine learning is to build robots or computer programs that can learn without direct human intervention and take initiative in accordance with what has been learned in a variety of situations.

How do experts teach robots something?

In order to be able to teach machines something, we must use learning algorithms. Machine learning algorithms are divided into two main categories, guided and unguided.

1. Guided machine learning algorithms

In this way, the data and examples taught to the machine are pre-categorized. This categorized data helps the robot predict and execute the closest appropriate response in new situations. In this method, the actual output is compared with the output specified in the algorithm to correct the errors that occurred. Robots playing chess with humans are an example of this guided learning pattern.

۲. Undirected machine learning algorithms

Unlike the previous method, there is no news of categorized data and no predefined answer. This type of machine learning can be likened to a child being left in the woods to explore and explore on his own. In this method, the algorithm does not specify the answer as the correct answer and the machine itself has to check the data to find the answers.

There are two other categories that are a combination of the two main categories and are described below.

3. Semi-guided machine learning algorithms

In this method, which is a combination of the previous two methods, a small part of the educational data is categorized and the rest is not categorized. In this case, the robot is like a child who has been given a road map and has to find his way by reading the map. Interestingly, such an approach increases the accuracy of learning as well.

This learning method is chosen when it is necessary to categorize a piece of data.

4. Enhanced machine learning algorithms

This method is similar to the trial and error method. Whenever a machine learns something and reacts accordingly, it receives a reward and positive feedback. These positive feedback are recorded and help the robot to use the same reactions in the future. In fact, these positive feedbacks, which are categorized according to machine performance, reinforce learning.

What is the use of machine learning?

What has made machine learning so important in today’s world is its massive production and storage. The more data we generate, the more we need machines to extract, analyze, and classify them.

Learning machines allow us to operate faster and cheaper. These machines allow businesses and companies to look for something useful in the data mine or to avoid unknown risks.

In addition, machine learning by imitating human behavior allows us to leave many common and repetitive tasks to machines. Face recognition, guessing and suggesting words as we type and edit texts are just a few of the familiar applications of machine learning in our daily lives.

Who uses machine learning?

Most organizations that deal with large amounts of data use these machines. Here are some examples.

1. financial services

Banks and other financial institutions use machine learning to achieve two main goals:

  1. Identify important data from the data set, such as identifying investment opportunities or identifying fraudsters;
  2. Identify high-risk or bad customers.

۲. government institutions

Governments are also constantly faced with a wealth of data. For example, security cameras that record millions of traffic every day. In addition to the security uses of the learning machine, the government is also improving the productivity of its service providers. The learning machine also performs many everyday tasks instead of humans. This saves money.

3. Health Department

Using machine capabilities to collect and monitor patient symptoms is one of the important applications of machine learning in this field. The learning machine also speeds up the diagnosis process.

4. Store services

Websites that offer you a quote based on previous searches use machine learning technology. The managers of these stores collect the records of your visits and purchases, and this data is used later to both improve the supply chain of the product and to make the advertising campaigns more effective by getting to know the customers better.

5. Oil and gas industry

The oil and gas industry uses machine learning to identify new mines, predict sensor failure, and more accurately distribute oil resources on the oil and gas transmission network. The application of learning machine in this field is expanding.

6. Transportation

Analysis of transportation data is essential for freight companies and public transportation entities. Identifying and predicting traffic patterns on different days of the year to select the best and fastest transfer method is one of the applications of this technology.

What is the difference between machine learning and data mining and deep learning?

All three methods pursue one goal (gaining knowledge and extracting patterns and understanding the relationships between data for better decision making), but each approach to achieving this goal is different.

To better understand this difference, it is better to look a little at data mining and deep learning.

1. Data analysis

Data mining can be called the basic foundation of all other methods of data processing. In this method, we use old and traditional statistical methods to analyze and process data. Data mining is done both by humans and by machines.

۲. Deep learning

Deep learning, by combining machine learning as a neural network and data mining techniques, seeks to identify hidden and deep patterns between data. This method is used today to identify objects in an image or words in sound. Researchers in the field are now seeking to use deep learning to detect diseases very early, to automatically translate languages ​​without the use of a human interpreter, and to address some of the most important and difficult occupational and social problems.

Now we can see how machine learning differs from the two. In machine learning, we seek to automate the data mining process. We want to teach the machine that it can process and learn data on its own without human intervention, or at least with minimal intervention.

What knowledge do we need to enter the field of machine learning?

Machine learning training

If you are interested in this field and your heart wants to work in this field, you must acquire knowledge and skills. The most important of these skills is learning a programming language (preferably Python). In addition to this skill, you must have acceptable mathematical knowledge. The three areas of mathematics in which you should acquire knowledge are:

  • Linear algebra for data analysis, familiarity with matrices and the concept of tensor;
  • Derivatives and gradients, statistics and probability;
  • Differential and integral calculus for better understanding of algorithms.

How interested or familiar are you with this topic? What is your opinion about the future of this science and its role in human life?

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