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Machine Learning: Definition, How It Works, and The Type

Last updated: Aug 25, 2023

What is Machine Learning?

Machine learning (ML) is a branch of artificial intelligence. It’s a technology machine  with the ability to learn a collection of data and perform certain commands according to what has been learned.

ML was first introduced in the 1920s by mathematical scientists, Marie Legendre, Thomas Bayes, and Andrey Markov. An example of ML application is Deep Blue which was created in 1996 by IBM. Deep Blue was developed to be able to learn and play chess.


How Machine Learning Works

How it works varies depending on what kind of learning techniques you want to use in ML. However, the basic principles of ML are more or less the same, namely data collection, data extraction, selection of learning models or algorithms, selected models or algorithms, and evaluation of the results of ML.

ML requires data to be learned. Based on this knowledge, it can process data and improve its accuracy. This makes ML that is often used to produce data with a better level of accuracy.


Types of Machine Learning Model

The types of machine learning models are as follows:

Supervised Learning

Supervised learning is a model or algorithm used in making ML. This algorithm requires labeled data to build a model with a level of accuracy that can increase over time. The more this model processes data, the higher the level of accuracy will also be.

Unsupervised Learning

Unsupervised learning is a learning model that trains unlabeled data. This model can extract data, process data, find patterns, and classify data automatically. Unsupervised learning is useful in pattern recognition, detecting anomalies, and grouping data into categories.

Reinforcement Learning

Reinforcement learning is a model that can make machines work automatically to determine the ideal step to maximize the performance of the algorithm. Reinforcement learning algorithms are commonly used in video game applications but are rarely used in business.


Advantages and Disadvantages of Machine Learning

After providing an explanation regarding the meaning, history, and types of ML model, then we will explain the advantages and disadvantages.

Advantages of Machine Learning

The application of ML provides several benefits to facilitate human performance. Some of the advantages are:

  • Can handle and process various data formats with high complexity in a short time.
  • Able to identify trends and data patterns that humans might miss.
  • The results can be more accurate over time.
  • It can execute commands to make decisions without human intervention.

Disadvantages of Machine Learning

Besides the advantages, ML also has some disadvantages. Here is the list of the disadvantages:

  • The process of training and learning data in the early stages is time-consuming and expensive.
  • The process of finding accurate results and eliminating uncertainties will be difficult without expert assistance.
  • It requires a large initial investment.



ML is widely used in digital marketing to improve user experience. Some examples of ML implementations are as follows:

  • Chatbot: a combination of several artificial intelligence techniques such as ML, NLP, and others to process input text commands and provide appropriate responses.
  • Online Advertising: ML and deep learning are used to evaluate the content on web pages so that the ads shown match the interests and relevance of visitors.
  • Digital Assistants: Google Assistant, Apple Siri, and others can help humans through voice search from ML and NLP implementations.
  • Recommendations: data processed by ML and deep learning can provide recommendations such as 'people also like' and others so that they can help people make decisions.
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