Almost all devices and systems around you already use machine learning technology. The reason is that machine learning is the main foundation for developing revolutionary technology. Devices or systems that use machine learning are considered superior because they can recognize patterns and data independently without manually entering commands.
The question is, what is meant by machine learning? What does the application of machine learning in technology look like? Find the answer by reading this article to the end!
What is Machine Learning?
Let’s start by learning about machine learning and this system’s basic principles.
Definition of Machine Learning
Machine learning is a system or machine designed to learn automatically without needing to get directions from the user. This technology was developed based on statistics, mathematics and data mining. Combining these three scientific disciplines allows a machine to learn through data analysis without needing to be reprogrammed manually and enter specific commands into it.
Machine learning can access and utilize existing data to learn independently. Machines can perform various tasks that have been programmed according to their accumulated knowledge by studying the available data.
History of Machine Learning
Even though it is still relatively new, the term machine learning has been introduced since the 1920s. At that time, several mathematical scientists such as Adrien Marie Legendre, Thomas Bayes, and Andrey Markov introduced the basics and concepts of machine learning. Since then, machine learning technology has continued to develop rapidly.
One famous example of the application of machine learning in history is Deep Blue, which was developed by IBM in 1996. Deep Blue is a machine learning system designed for learning and playing chess. This system has been tested by playing against professional chess champions and successfully winning the tournaments in which they participated.
Machine learning technology now helps humans in various fields, mainly everyday life. For example, the face unlock feature can unlock smartphone devices or advertisements matching users’ interests and preferences when browsing social media or e-commerce websites. These advertisements are the result of data processing using machine learning technology.
Basic Principles of Machine Learning
There are three basic principles in machine learning, namely:
- Data: Data is the main component of machine learning. Machines analyze and identify patterns from given data to learn the relationship between input and output.
- Algorithms: Algorithms are needed to process data and produce machine learning models. Machine learning algorithms instruct machines to analyze data and make predictions or decisions based on the patterns found.
- Learning and Improvement: Machines continually learn and improve their performance as they gain experience and data. From the learning process, machines can optimize models and increase their accuracy and ability to make decisions or make predictions.
Machine Learning Learning Techniques
There are three ways machines learn through machine learning systems: supervised, semi-supervised, and unsupervised. How’s the explanation?
1. Supervised Learning
Supervised learning techniques mean that the machine receives data labelled with that information. This technique aims to provide a target for the output by comparing it based on the history of the machine’s learning experience.
For example, you have a collection of films labelled by category, such as comedy, horror, or action films. When adding a new film to the streaming application, the machine will place the new film according to the specified genre category.
2. Semi-Supervised Learning
This technique is not much different from supervised learning. However, you use labelled and unlabeled data to train the algorithm in semi-supervised learning. Typically, you use a limited amount of labelled data with a more significant amount of unlabeled data.
An example of using semi-supervised learning is identifying a person’s face in a webcam or smartphone camera.
3. Unsupervised Learning
The unsupervised learning technique is an approach where the data used does not contain information that can be applied directly. Unsupervised learning aims to find hidden structures or patterns in data that do not have labels. In unsupervised learning, you have no previous reference data.
For example, you have never purchased a movie on a movie streaming application. However, you purchased several movies and want to combine your movie collection to make it easier to find. To create categories, you then try to identify films with certain similarities, such as genre or actors.
Application of Machine Learning in Technology
At a glance, it is clear that machine learning is commonly applied in technology. What is an example of its application? Let’s look at the various phenomena below:
1. Classification and Prediction
Machine learning has wide applications in classification and prediction. In classification, machine learning groups data or objects into predetermined categories based on identified patterns or features. For example, facial recognition. Machine learning algorithms can classify facial images based on the detected facial features.
Apart from that, machine learning is also used for predictions. Machine learning algorithms use historical data to make predictions or estimates regarding future values or events. For example, weather predictions. Machine learning algorithms analyze past weather data to predict future weather conditions.
In both cases, machine learning uses machine learning techniques and models such as Naive Bayes Classifier, Decision Tree, or Support Vector Machine to classify or predict patterns or relationships in the data. Learn more Artificial Intelligence (AI).
2. Pattern Recognition
Machine learning is applied in pattern recognition as in the example below:
- Image analysis: Pattern recognition is used in digital image analysis to study images automatically to gather information.
- Seismic analysis: Seismic analysis involves the study of natural events, such as earthquakes, that can affect rocks, buildings, and soil. Pattern recognition is used to discover and interpret patterns in seismic events.
- Health: Pattern recognition is used in the health sector to improve health services. For example, medical personnel may use patient data for further analysis.
- Fingerprint identification on smartphones: Modern smartphones have a fingerprint identification feature to increase security so that users can access the smartphone or application after the fingerprint verification process.
3. Data Processing
The application of machine learning in data processing is extensive, significantly contributing to optimizing analysis and decision-making. Here are some examples of implementation:
- Data preprocessing: In this stage, machine learning is used to clean, filter, and transform the data into a more suitable format for the analysis process.
- Data classification: Machine learning is applied to classify data into predetermined categories through pattern recognition or identification of objects in images.
- Modelling and Prediction: Machine learning is applied in building models based on historical data and making predictions on new data.
- Clustering: Machine learning is used to group data with similar characteristics into specific groups.
How? Do you understand machine learning after reading this article? In conclusion, machine learning is a system designed and developed to carry out tasks independently without orders from humans. Machine learning can be applied to anything, including creating e-commerce websites.
On an e-commerce website, you want to display products relevant to users needs based on their browsing history, preferences or purchases. You also want to build a website safe from cyber-attacks and other suspicious actions. The application of machine learning in website creation certainly requires sufficient expertise.
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