Artificial intelligence (AI) terminology has appeared more and more lately. AI plays a role in the development of technology, especially in the automation of various things. AI consists of 7 branches. The branches are machine learning (ML), natural language processing (NLP), vision, speech, expert systems, plans, and robotics. Those branches aim to facilitate the learning and development of AI which has a very broad scope.

So, the discussion of this article will focus on just one branch of AI, machine learning. Have you ever heard or read about this term? How far do you know about machine learning? Come on, let’s discuss it here.

Understanding machine learning

Machine learning is a technology developed to be able to study data by itself so that it can perform tasks without user direction. This technology is commonly used to perform several tasks such as clustering, classification, regression, and dimension reduction. We often encounter the benefits of machine learning, such as spam detection, facial recognition, voice recognition, etc.

The goal of machine learning is to build models or systems that can learn from data and then make accurate predictions or decisions. What is meant by data here is information or input that will be learned by the model. While the model is a mathematical representation of the learning process used to study patterns from existing data.

Apart from data and models, the components that are also needed to create machine learning are algorithms. This technology requires algorithms to be able to perform tasks independently without human intervention. A machine learning algorithm is a set of mathematical instructions or procedures for developing machine learning models. That model can then be used to predict results based on the data provided.

The data used in ML is Big Data, which is a collection of data that is very large, varied, and growing very fast. The workflow includes data selection which is divided into training data, data validation, and test data. After that, the work steps are to build, validate, and test the model based on those three parts of the data.

The term machine learning was coined by Samuel Arthur in 1959. This computer scientist from the United States defines machine learning as the ability of computers to learn without having to be programmed explicitly.

Examples of machine learning usage

Currently machine learning is increasingly being used in various industries. The reason, of course, is that this technology can speed up business processes and provide more optimal service to consumers. Here are some examples of using machine learning.

Marketplaces use machine learning to classify products, provide product recommendations to customers, product advertisements, chatbots, etc.

Social media uses machine learning for post archiving, facial recognition, fingerprint verification, providing “people you may know” recommendations, etc.

Smartphones or iPhones use machine learning to enhance user security with patterned password methods, fingerprint recognition, and face recognition.

Health devices apply machine learning to monitor things like oxygen levels, heart rate, etc. so the doctors can assess the patient’s health in real-time.

The financial industry uses machine learning to do fraud detection so that customers become safer and more comfortable. Machine learning can also provide information to investors on when to sell and buy.

Advantages of using machine learning

There are at least 4 advantages when a company uses machine learning, they are:

1. Accuracy: able to make accurate predictions and decisions based on data
2. Efficiency: optimize the process of making decisions or predictions more quickly
3. Flexibility: can be used for a variety of tasks and applications
4. Scalability: can process data on a large and complex scale

Due to the many advantages provided by machine learning, more and more industries are adopting this technology. It means, the wider the career opportunities that require machine learning skills. Are you interested in taking this opportunity? Come on, learn the skills here.