How to differentiate machine learning types and their purpose

Types of machine learning and their most frequent applications
June 15, 2023 by
Boot & Work Corp. S.L., Nerea Cacheiro Barroso

Artificial intelligence (AI) has enabled a wide range of modern technologies, from language-savvy chatbots and autonomous vehicles to recommendation systems and voice assistants. This is all thanks to the breakthrough of machine learning, a subfield of AI that trains machines to learn.

Future of the artificial intelligence AI

Jeff Hawkins, a computer engineer and founder of Handspring and Palm, in the opening chapter of his acclaimed book "On Intelligence," published in 2004, challenged the conventional wisdom about AI.

He argued that many believe AI will eventually be able to deliver on its promises as computing power increases. However, Hawkins argued that AI suffers from a fundamental flaw in that it doesn't grasp the essence of intelligence or comprehending.

A glance at the history of AI and its fundamental principles reveals how the field has lost its way. 

In the same book, Hawkins outlined human intelligence as the ability to forecast the future based on previous experiences. And that is exactly what machine learning does.

    Exploring the Basics of Machine Learning

      A type of artificial intelligence known as machine learning uses algorithms that find patterns in data that has already been stored and use those patterns to produce new data to enable machines to learn on their own.
      • Machine learning algorithms enable computers or other devices to analyze problems and find solutions in a manner similar to humans.

      What is the difference between machine learning and deep learning?

      Deep learning (DL), a new branch of machine learning that employs methods that are even more close to those used by human neural networks than machine learning, first appeared in 2010.

      Types of machine learning

      There are currently three distinct types of machine learning, which differ from one another in terms of the algorithm they employ. The two most popular types of machine learning are:
      •  supervised 
      • unsupervised

      but reinforcement machine learning is also a type of machine learning algorithm.

      Guided machine learning

      Algorithms that can "learn" from data handled by a human are used in this kind of machine learning. In other words, in order for supervised machine learning algorithms to generate additional knowledge on their own, the data must first be classified, labeled, and input into the system by a person.

      Supervised machine learning algorithms can predict the future and even make complex choices based on the labels that have been applied to the data. This kind of technique can be used with either classification or regression data as input.

      Machine Learnings Guide

      Self-organizing machine learning

      Unsupervised machine learning algorithms run autonomously and don't need any prior knowledge to "learn" or solve issues. Clustering algorithms and association algorithms are the two categories of unsupervised machine learning techniques. Unsupervised machine learning is used to provide computers with the ability to recognize patterns in messy, unlabeled data sets. The algorithm is in charge of identifying patterns in the data so that it can then come to its own "conclusions."

      Interactive machine learning

      Algorithms for reinforcement machine learning are programmed to learn from the past. In other words, these algorithms can work out problems based on the outcomes or triggers of circumstances that are similar to those they have already encountered.

      Reward-based machine learning involves putting a machine through a series of decisions-based assessments. The system is "rewarded" if it chooses the right course of action. The algorithm can make the right choices on its own after a few tests.

      How is machine learning being applied?

      As we've already seen, machine learning and artificial intelligence are used to improve the way that data-driven decisions are made and to produce intelligence from advanced data analytics. Predictive analytics, process automation, and process optimization are additional common applications.

      Machine learning, however, is one of the key technologies advancing digital transformation and scientific advancements and has real-world applications in all knowledge fields. 

      Let's look at a few of them. 👇🏼

      Smart cars

      Vehicles are becoming more intelligent, even though we have not yet achieved "Knight Car" levels. Machine learning algorithms, for instance, are used to create assisted driving. According to a recent IBM study called Automotive 2025, by the year 2025, cars will automatically adjust their radio, temperature, seating position, and other features to the preferences of the driver.

      Medical research

      One of the foundations of scientific research, particularly in medicine, is machine learning. For instance, machine learning is being utilized for the early detection of diseases such as cancer or various forms of respiratory disorders, which have a better diagnosis when caught early.

      Natural language processing (NLP)

      One of the most common and beneficial applications of machine learning is in natural language processing.

      Folksonomy is one example of a PLN algorithm that can classify information, recognize patterns in the data, and derive inferences from information written in natural language—the language that humans use.

      Multiple industries utilize PLN for document management and research based on massive amounts of natural language data. Another application of natural language processing is voice assistant bots.


      Personalization is unquestionably one of the most widespread uses of machine learning and artificial intelligence in:

      •  marketing
      • e-commerce
      • and any form of economic activity established in the digital domain

      Machine learning algorithms are used by businesses to automatically identify consumer behavioral trends and to provide customers with customised content and, particularly in e-commerce, personalized recommendations.

      Search engines

      Machine learning is also used by search engines to customize their material. Each user receives a different set of results from search engines depending on their past searches, online activity, or demographic information like location.

      Try it! Give a coworker a term to look up on Google with their phone, then look it up yourself to check whether Google returns the same results.


      Additionally, algorithms are used in cybersecurity, particularly to enhance the functionality of antivirus programs, which, by using ML algorithms, improve their capacity for anomaly detection.

      Since Hawkins wrote On Intelligence, artificial intelligence has advanced significantly. The author was incorrect, and machine learning, deep learning, and their real-world applications are evidence of this. Machine learning algorithms are increasingly becoming more similar to the human brain, even if machines still cannot think like humans.

      Understanding the different types of machine learning and their applications

      Machine learning (ML) is a critical component of AI, divided into three types: supervised, unsupervised, and reinforcement learning. Supervised ML requires labeled data for training, making predictions, and decisions. Unsupervised ML identifies patterns in unlabeled data, while reinforcement learning learns from outcomes through rewards. Applications of ML include smart cars, medical research, natural language processing, personalization in e-commerce, search engines, and cybersecurity. These technologies drive digital transformation, offering advancements across various industries by enabling intelligent data-driven decisions.

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      Boot & Work Corp. S.L., Nerea Cacheiro Barroso June 15, 2023
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