7 Examples of the Application of Machine Learning in Different Fields

There are many examples of the application of Machine Learning (ML) in various fields of work. Machine Learning itself is one of the fields of study that plays a major role in the development of technology today. There are already many industries that apply Machine Learning in their work processes.

So, what are some examples of the application of Machine Learning around us? Before knowing more about it, it’s good if you understand what Machine Learning is in more depth first. Let’s see the full explanation below!

What is Machine Learning?

Machine Learning (ML) is a technology developed so that a machine can learn by itself without direction from its users. Machine Learning will learn to use data and past experiences while identifying patterns to make predictions with minimal human intervention. Machine Learning itself is a field of study that is part of the field of artificial intelligence (AI) studies.

Machine Learning algorithms build models based on sample data, otherwise known as training data, to make predictions or decisions without needing to be explicitly programmed to do so. Machine Learning Algorithms are widely used in various complex applications when conventional algorithms find it difficult to perform the required tasks.

Examples of the Application of Machine Learning in Different Fields

The development of Machine Learning (ML) has changed various industries and the way data is handled. Algorithms will be ‘trained’ to find patterns in large amounts of data to make decisions and predictions based on logic, enabling machines to learn and complete tasks without explicit programming. The following are some examples of the application of Machine Learning in various fields in our lives.

1. Health

ML has been used for various purposes in the health sector. ML deployments enable healthcare providers to generate large volumes of data and enable critical clinical decisions to be made.

The ML algorithm can be used to detect patterns associated with a patient’s disease and health condition by studying thousands of health care records and data from other patients. In addition, the ML algorithm can also help detect tumors on scans and identify possible health problems that could occur.

The ML algorithm can also be applied to various health aid devices. For example, wearable devices and sensors to monitor specific things from a patient’s steps, oxygen level, to heart rate can generate a large amount of data that allows doctors to assess a patient’s health in real-time and provide notification to doctors. As a result, any problems that arise in a patient can be handled immediately.

Read Also: 5 Roles of Information Technology in Business in the Digital Era

2. Transportation and Automotive

In recent years, the application of Machine Learning in cars or AI-based systems in cars and other vehicles has increased drastically. In 2015, the adoption of AI-based systems in cars only reached 8%, but that number is predicted to increase to 109% by 2025.

Cars that apply ML have become a benchmark and are sought after by people today as an alternative to conventional cars. Although the price is still relatively high, its predictive mechanism can accurately notify drivers of possible spare parts, driving routes and directions, emergency and disaster prevention protocols, and much more.

3. Finance

There are various applications of ML that can facilitate and make a profit in the financial sector. For example, algorithmic trading methods that are now widely used in finance refer to the use of algorithms to make better trading decisions. ML can provide a variety of information that allows investors to identify when to sell or buy.

ML can also be used to analyze millions of data sets in a relatively short time to improve trading results without needing to be explicitly programmed to do so. This can help detect illegal transactions, such as fraud, and make online transaction processing more secure.

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4. Agriculture

Agriculture is one of the fields that benefit the most from the application of ML. Farms can apply ML to a wide range of needs, from detecting weeds and diseases, and predicting crop yields and quality, to collecting data, providing information, and making predictions regarding livestock production.

Precision agriculture (PA) is a dream that has come true from the wishes of farmers who want their agriculture to be more successful. When farmers can apply PA as precisely as possible, it means that the farm has been running successfully. To be able to apply the concept of PA, farm management must be able to apply ML algorithms and Internet of Things (IoT) devices in their daily work.

5. Cybersecurity

ML has become a vital technology for cybersecurity and can make organizational security run more smoothly. By implementing ML, cybersecurity systems can analyze patterns and learn from these patterns to help prevent cyberattacks and respond to changing the behavior of cybercriminals. ML deployments can also help cybersecurity teams be more proactive in responding to ongoing cyberattacks.

6. Marketing

The application of ML in the field of marketing is one of the steps to making the marketing strategy effective. ML can understand large amounts of data faster and much more effectively than humans, and it provides marketers with opportunities to make important decisions based on big data.

In addition, ML can also be used to make predictions about the marketing strategy that will be carried out. The AI and ML-powered system will efficiently simulate the marketing strategy that has been created based on the past data that has been collected to deliver the desired results.

Read Also: 5 Effective Marketing Strategies for the Millennial Generation

7. Manufacturing

Manufacturing has many aspects of work that can be improved by implementing ML, from robot-driven assembly lines to intelligent systems that can predict machine breakdowns. ML will also help solve supply chain problems across a wide geographic location and streamline the online product delivery process.

Now, we hope you all understand the examples of the application of machine learning in various fields of work that are around you. AI-based systems such as Machine Learning can indeed simplify various kinds of work. Therefore, one of the ML technologies that can be used to simplify our work is the OCR application from AdIns. Contact AdIns now for more information!