What is Machine Learning?
What is Machine Learning?
Machine Learning, known in Turkish as 'makine öğrenimi,' is a technology that enables learning through experience, just like in humans, without direct programming. Thus, machine learning is used as a product of artificial intelligence that allows systems to learn automatically and develop themselves more over time. In machine learning, the process is possible through the programming of observations, experiences, instructions, and similar data, so that better decisions can be made over time. The fundamental purpose of this learning method is to enable the machine to learn on its own without human interaction and to make necessary corrections as a result.
Machine learning, a sub-product of artificial intelligence, is used as a learning method, just like deep learning. As a result of this process, the machine gains the ability to think and make decisions like a human, or carry out tasks. At this point, the human brain is used as a reference for development, and a neural network is used. This neural network system is implemented with deep learning. Deep learning is used here as a specific form of machine learning. It can classify, just like in humans, thanks to deep learning, which can confirm the accuracy of information by itself.
Machine Learning, or its Turkish translation, 'makine öğrenimi,' is referred to as a subset of artificial intelligence focused on creating systems that support improved performance. The question of 'what does Machine Learning mean?' is answered in this way. Here, artificial intelligence is a term encompassing a derivative of human intelligence. Artificial intelligence and machine learning are partially referred to with the same functions. Although they are often used for the same processes, these two concepts do not mean the same thing. Machine learning products do not encompass artificial intelligence, and likewise, artificial intelligence products are not a result of machine learning.
Machine Learning has been used in many applications from the past to the present. Developers working on Machine Learning continue to create new models based on information and data in various fields. Machine learning, also known as machine learning or by its other name, is referred to as an AI application. However, the process essentially progresses in the form of teaching the computer automatic operations.
Types of Machine Learning
Machine Learning methods are basically categorized under 4 main headings. Each title serves to meet a different need. The Machine Learning methods or types are as follows:
- Supervised Machine Learning Algorithms: In supervised machine learning, past knowledge and experiences are applied to new data. This algorithm system enables machines to operate with a specific set of rules. Since it also has a structure that supports the analysis of errors, it allows for comparison. In short, it is possible to say that the supervised machine learning algorithm has a task-oriented system structure.
- Unsupervised Machine Learning Algorithms: The unsupervised machine learning algorithm is used in cases where the data is not classified. In this algorithm, the data is analyzed, and it focuses on finding a hidden structure. Unsupervised machine learning houses a system structure focusing on data.
- Semi-Supervised Machine Learning Algorithms: This algorithm uses both classified and unclassified data. Semi-supervised machine learning algorithms, also used for learning, increase the machines' learning rate more than other supervised and unsupervised algorithms.
- Reinforcement Machine Learning Algorithms: The reinforcement learning method is used more for the purpose of interaction with the environment. This algorithm helps both in identifying errors and in determining the machines' ideal behaviors.
What Are the Examples of Machine Learning?
With the answer to the question of what Machine Learning is and what it does, examples of this system are also among the curious topics. Machine learning has popular examples such as the Facebook News Feed. Here, machine learning offers a summary feed for each person who is a member of Facebook. It does this by presenting the publications in the friends list for purposes like liking or commenting. If a Facebook member no longer interacts with the news presented, it ensures continuity by bringing new news sources. At this point, it is possible to say that machine learning has vital importance in self-renewing tools. Many examples can be given for machine learning, just like on Facebook. Machine Learning, frequently used in corporate applications, offers an efficient experience to individuals with its studies.
In the Customer Relationship Management (CRM) system, the machine learning system is used to analyze electronic mails. At the same time, this system is also effective in responding to messages. Learning systems are used to determine the characteristics of employees with the Human Resources (HR) system and find effective candidates for open positions. In another example, the Business Intelligence (BI) system, important data points for users can be automatically defined. Machine learning methods are used to assist at this point in the software. Machine learning is generally presented with a structure that supports basic business uses. With machine learning, which speeds up the decision-making process, visibility in the business life of workplaces also increases. It is possible to say that the collaboration rate between different departments becomes more efficient with machine learning.
What Are the Business Goals of Machine Learning?
Machine learning has some business goals, which is widely used in areas ranging from banks to online shopping sites. These business goals are briefly as follows:
- Services are offered to customers by considering the demands and desires of the customers. For a successful marketing process, individuals need to offer products to the right people at the right time. Therefore, correctly segmenting customers is of great importance to achieve the targeted result. With machine learning, this situation becomes possible, and it also meets the needs of every customer by dividing customers into specific groups. Demographic characteristics, proximity level, or behavioral patterns play an active role in the classification. Businesses have more suitable customer profiles with this system, which is very useful for companies to present their campaigns appropriately.
- With machine learning, customer loss can also be modeled. Gaining new customer profiles by a company, keeping the satisfaction of existing customer profiles stable are among the details that cost companies. The customer loss model also allows for the presentation of which group of customer profiles left the business and the possible reasons for this situation. Customer preferences, which always have more selection power from businesses, can change. Therefore, businesses' ability to have foresight about customer loss is of vital importance.
- It is possible to say that machine learning has a structure that supports electronic commerce and financial services with its features. In addition, machine learning stands out with its software potential in sectors such as construction, health, energy, or science. Thanks to machine learning, many processes such as 2D and 3D structure plan models or social media image content can be performed.
- With machine learning, customer lifetime value modeling can also be done. However, with this feature, which is important not only for this sector but almost all sectors, it is possible to determine customers, understand their needs and desires, and provide appropriate services. At the same time, this feature can also predict the potential income of the business."
How Does Machine Learning Work?
Teaching a computer to respond automatically is possible thanks to machine learning. This concept, which means teaching it to process without the need for any coding, just as if it were done by a human, allows the computer to perceive data and predict. The question of what Machine Learning is can be answered most succinctly in this way. With advancing technologies, this concept is now actively used in daily life. For example, retail companies can determine which products they need to send to which stores and in what quantities thanks to this system. Additionally, in some places, doctors can also get help from machine learning when determining treatment methods. Artificial intelligence is added to an ordinary system using machine learning, and as a result, this system now transitions to a form that can make various predictions and develop information.
The machine learning system makes machines much more advanced and intelligent by teaching them information. Development continues with new experiences in this newly developed system. The initiation of machine learning starts with the provision of correct samples, experiments, and instructions to the system. Then, the necessary tests are carried out, and the results are evaluated. As more data is entered into the machine, the system is further developed. This way, it becomes possible for machine learning to occur on its own within the process, and the most suitable answers to be provided.