What is machine learning and how does it affect our lives?

 

If you think that you do not know what machine learning is, then you are wrong. Look around you and you will discover that the machine is now controlling all aspects of life. Because of machine learning, human lifestyles have changed at an alarmingly fast pace. Human communication has shifted from real life to virtual reality and through social networking sites, and after going to markets and selling stores, we started to settle for electronic shopping via the Internet.

 

By predicting traffic using Google Maps, cars drive humans to operate autonomously without any manual intervention. Image analysis systems help doctors discover hidden tumors that they have missed, and relying on software and electronic writing has become a primary option instead of writing on files and paper documents.

 

There are many examples of machine learning, the most famous of which is the robot Sophia, and how its built-in cameras allow it to see and recognize human faces, understand their expressions, recognize their features, and address them.

 

Today, technology has reached the furthest stage of development. For us, digital assistants like searching the web as we browse like Siri, and play music in response to our voice commands, websites can advise us through pop-up ads for products, movies, and songs based on our likes and what we've bought, watched or listened to Before. And while we're doing all this, we shouldn't forget about the robots cleaning our floors.

 

Thanks to machine learning, email detectors can prevent spam from reaching our inboxes. Look around you well, and you will see how the machine was able to expand its territory and impose its control and restrict the human element to its lowest levels by intervening in many areas, but all this was in order to achieve a lofty goal, which is to serve humanity itself.

 

What is Machine Learning

 

Machine learning is a branch of artificial intelligence, which focuses on building self-learning applications from data fed to it in advance to gradually improve its accuracy without being manually programmed. In order to create a good machine learning system, several key aspects are relied upon, which include data preparation capabilities, basic and advanced algorithms, what is known as automation and iterative processes, in addition to scalability and sparse modeling.

 

In data science, an algorithm is a series of statistical processing steps. In machine learning, there is a stage called training, which means training algorithms to find patterns and specifications amidst huge amounts of data, so they can make decisions and make predictions while minimizing or completely absent human intervention. The better and more comprehensive the algorithm, the more accurate the decisions and predictions will be thanks to its ability to process larger amounts of data.

 

In recent times, those interested in the science of artificial intelligence have increased their passion to see if the machine "computers" can learn from the data available to them, and how they will behave when exposed to some emerging data models. And to simplify all the complex phrases into understandable words, remember only what happens in the advanced airport halls, when the identity of travelers is identified by recognizing and distinguishing between facial features, and dealing on the basis of the data that was previously stored and how to act in the emerging situations that occur to it.

 

Why do people care about machine learning?

 

The growing interest in machine learning is due to the same reasons that have made data mining more popular recently, the ballooning volumes of data available than ever before, electronic computing processing that is cheaper and less expensive than relying on the human element, in addition to the high capabilities in storing data instead of Use of documents and papers.

 

All of this means that there are better opportunities for increased productivity, automatic model detection, and the ability to process and analyze more complex big data to produce faster and more accurate results. The industrial, commercial, and service sectors and institutions will enjoy certain opportunities to achieve the highest profit return, conduct their business, and make decisions automatically and accurately to avoid unexpected risks caused by the human element.

 

Who uses machine learning today?

 

Most of the large factories and organizations that deal with huge amounts of data have well understood the value of machine learning technology. By extracting new ideas from the underlying data, these organizations are able to work more efficiently and gain an advantage over their competitors.

 

In the financial sector, banks and investment companies use machine learning techniques for two purposes: to help the investor to inform him of stock prices and the appropriate time to conduct trading operations. While the other goal is to identify important insights and ideas to prevent fraud and fraud using electronic monitoring, and by attracting important data to identify customers who have a high-risk identity that may pose any threat to them.

 

In the field of medicine and health care, patients and injured people can be diagnosed by analyzing available data and offering them the best treatment. In the oil and gas fields, the search for new energy sources and the analysis of minerals in the ground simplifies the distribution of types of oils to make it more efficient and cost-effective.

 

Government agencies, public safety organizations, and utilities are harnessing machine learning to protect people and funds, detect fraud, and reduce identity theft and identity fraud attacks.

 

In online stores, machine learning is relied upon to analyze people's historical data to know their interests and sales sites they have visited recently to display similar products that they might like. Also, machine learning can be relied upon to implement expanded marketing campaigns, develop new price plans, plan the supply of goods, and improve business transactions with customers.

 

In transportation, machine learning is relied upon to determine directions and detect valid routes, and routes to avoid in order to avoid potential risks. Machine learning is a feature of artificial intelligence that gives computers a chance to learn on their own without being explicitly programmed, as it focuses on developing applications and programs that change regularly every time, they encounter new patterns of data.

 

If you feel that you are still confused about understanding machine learning, remember what happens when you add some new products to the shopping cart while purchasing from e-sale sites, then you will notice that the site has started offering similar offers and products that you may also like. How did he know that on his own? The answer is thanks to machine learning, which allows computers to learn on their own, understand developments, deal on the basis of previously provided data, and act on their own without the need for human hands.