Recently, new terms have begun to appear in the field of technology, and they have become very popular due to their great importance. Among these terms are the terms “artificial intelligence,” “deep learning,” and “machine learning,” which we will talk about today in this article.
Most likely you have heard of these terms, and you may not know their meaning like many, so let me simply explain them to you. Let’s talk about the difference between a person and a machine. A person has a very complex mind that makes him able to act and think in different situations. As for the machine, it is programmed to perform certain functions specified by the person.
So the machine executes and the human thinks, if we assume that you have programmed a robot to bring you a specific thing from a specific place in the house. If you ask him to bring you this thing, he will go to the place you programmed him to, and if he does not find it in the exact same place, he will not bring it and will stop.
But if you tell someone to bring you the same thing from a specific place in the house, if he does not find it, he will look around this place; perhaps he will find it lying on the ground or hidden under something placed on it by mistake. This is the essential difference: a person thinks and acts quickly and flexibly if he encounters a sudden problem, while a robot or a machine, if it encounters a problem that it has not been programmed to solve before, will stop at it and not act. Hence the idea of machine learning, whose goal is to give machines the ability to teach themselves, analyze different types of data, and find quick solutions without the need for human intervention in every matter or problem that occurs in front of the machine.
What is machine learning?
Machine learning is a field of artificial intelligence that refers to the ability of information technology systems within machines to independently find solutions to problems by perceiving different patterns in data. This is done without human intervention or guidance, as the machine is taught to perform tasks on its own and acquires the ability to solve future problems. When it finds a specific problem that it has not encountered before, the machine does not stop completely but searches for any alternative solution.
By applying machine learning, IT systems can:
- Finding and extracting relevant data.
- Make predictions based on data analysis.
- Calculate the possible probabilities of the given outcomes.
- Adapt to developments independently.
- Improve processes by recognizing different patterns.
How is machine learning done?
Machine learning is like human learning; just as children learn, so does the machine. For example, when a child is shown a picture of a living being and the name of the living being is mentioned, then a picture of another living being is shown and its name is mentioned, and so on. Then, when we show him a group of pictures of certain objects, he will learn to recognize and distinguish between them.
Machine learning happens in the same way, by entering certain data and commands so that the machine can learn to recognize different objects and distinguish between them. The program is provided with data and trained on it. For example, the programmer tells the machine that, for example, this particular object is considered a human being, but that object is not considered a human being.
The machine software receives sequential data from the programmer for different models of objects and considers this data as a kind of mark that algorithms use to improve their model. With each new piece of data entered into the system, the model is further refined, becoming clearer, until eventually the machine can clearly distinguish between the human model and other objects. But machine learning means much more than simply distinguishing between two different things, and this is considered a simple start to the application of machine learning, but we look beyond that.
The benefits of machine learning
Machine learning enables humans to work more creatively and efficiently. You can also assign some complex or somewhat tedious work to the machine, such as saving and filling out paper documents and invoices, or organizing and editing photos. In addition to these simple tasks, machine learning can perform more complex tasks, as it can identify error patterns, especially in industries that depend on continuous and error-free production.
Humans cannot always identify the error and its exact location, but machine learning can identify the error early and locate its occurrence, which saves a lot of money and time.
Machine learning programs are also now used in the medical field, as it is expected that in the future, applications will warn a person if he is prescribed a drug that his body cannot tolerate, taking into account the patient’s genetic requirements.
Types of machine learning
Algorithms play a very important role in machine learning, both through their ability to recognize different patterns in data and their ability to find solutions to problems. Machine learning algorithms are divided into:
1. Supervised machine learning
In this type, the machine is given a set of previous experiences by providing a number of clear and classified examples, so that the machine can later apply the classified examples that were introduced to it to the new data it encounters and thus become able to make predictions for future events.
This is similar to training the machine to differentiate between certain types of data, such as human images and things, where previous experience is built on this data, so that the machine in the future can recognize data similar to the images entered and form previous experience about it.
2. Unsupervised machine learning
A different set of data is entered into the machine, and this data is not classified, so the machine finds and deduces the similarities and differences between all the data that was entered, and thus the machine alone can classify the data, differentiate between them, and group the similar data together.
By applying this type to the example of images of people and things, a large number of images containing people and things are inserted, and the machine itself makes a connection between the images of people and some of the images of things and distinguishes between each of them.
3. Semi-supervised machine learning
Semi-supervised machine learning falls in the middle between supervised and unsupervised learning because it uses labeled and unlabeled data to train the machine.
Where a small amount of labeled data is used with a large amount of unlabeled data, which helps systems improve learning accuracy very significantly, and this type is applied in certain cases.
4. Augmented machine learning
In this type of learning, the machine is made to interact with the surrounding environment and discover errors by itself, as this method relies on the idea of reward and punishment.
Whereas the algorithms learn by interacting negatively and positively with any reaction that the machine should take in a specific situation, the machine takes steps and implements them, and based on the reaction from the surrounding environment, it can determine whether it made a mistake or was correct and works to develop itself on its own.
Machine learning applications
There are many applications of machine learning, some of which we will mention in this article. Machine learning technology is applied in many platforms that we all use, such as “Netflix” and “Amazon“, as well as in the facial recognition feature of the “Facebook” application. For you as a user, machine learning is reflected in the ability to tag people in the photos you post on Facebook, but in fact, Facebook has the largest database of people’s faces in the world.
This database is constantly being fed by users, and Facebook improves and trains the machine learning system using visual recognition. Also, in if you can notice the types of posts that appear to you, you will find that the types that you like and interact with always appear to you, and similar types appear to you as well. This is because the software was able to teach itself and know the types of publications that you like, and it was able to collect similar ones and make them appear to you as well.
YouTube also uses machine learning technology, and I tested it myself as a result of a certain thing I was doing, and here’s the story… In a previous period, I used to sleep at a specific time every day, and before going to sleep, I would play certain music to help me relax and sleep. When I open YouTube at any time of the day, I find my favorite music and songs that I listen to constantly during the day. As for when I open the YouTube application at the hour when I fall asleep, the first suggestion for a video is the music video that I listen to right before bed. YouTube has learned through machine learning technology that at this time of night I play this video, and it shows it to me at its first suggestion only at the hour I used to play it. Chatbots are also used in the field of customer service over the phone; these are automated programs that communicate with customers, and they can improve their cognitive abilities and forward more complex calls to communications personnel.
There are many, many applications of machine learning, and it is very clear that machine learning technology will move humanity to a great and important leap in all fields and make a big leap in all aspects of life. By applying machine learning technology, companies will not only be able to better satisfy their customers but also achieve better profit margins and reduce costs at the same time. Through machine learning, customers’ desires and needs can be identified, and this can be used in the e-marketing process to accurately identify potential customers for each product so the appropriate advertisement is made to reach the appropriate customer without any waste of money.
Are there job opportunities in the field of machine learning?
You may be wondering if there are job opportunities available in the field of machine learning, and the answer is yes, there are job opportunities available in the field of machine learning, to a large extent and for a high salary, and the demand for them will increase further in the coming years.This is due to the fact that as companies began to recognize the importance of machine learning and the enormous benefits it provides to us, the demand for machine learning specialists grew.Also, training courses in the field of machine learning are now available on various platforms on the Internet, free or paid.
You can take a step and learn more about this field, and I recommend “Kaggle” to you, as it is a large platform that includes the largest number of interested, educated, and professional people in the field of machine learning. as well as different companies that present problems they face with machine learning and ask for help from workers in this field. You can enter the site, create an account, and engage in a community of those interested in and experienced in the field of machine learning; this will help you a lot to enter and learn this field. As well as identifying the important training courses that you must obtain, by communicating with workers in this field and learning from their experience, you will get to know the labor market in the field of machine learning.
One of the most important sections on this site is the “Competitions” section, in which companies present the problems they face and also offer large financial prizes to whoever provides and implements the appropriate solution by writing a specific code that solves the problem. Now, in the beginning, of course, you will not be able to participate in these competitions, but after you learn and take training courses in this field and practice on it, you will be able to participate, even in some simple problems, or even just try, and it will be very beneficial for you.
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