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What is Machine Learning?

Updated: Dec 5, 2021

Everyone seems to be writing about Machine Learning and its impact on both humans and the advancement of Artificial Intelligence these days, whether you open your browser or casually scroll through a news feed. It is said that ML and AI are literally our future. So, what exactly is Machine Learning?

Machine Learning (ML) is a subfield of Artificial Intelligence (AI) and computer science that focuses on using data and algorithms to mimic how humans learn, gradually improving its accuracy.

While AI is a broad science that mimics human abilities, ML is a subset of AI that teaches a machine how to learn.

ML today is not the same as ML in the past due to advancement in computing technology. It arose from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; AI researchers wanted to see if computers could learn from data. The iterative aspect of ML is important because models can adapt independently as they are exposed to new data. They learn from previous computations in order to produce consistent, repeatable decisions and outcomes. It's not a new science, but it's gaining traction.

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While many ML algorithms have been around for a long time, the ability to apply complex mathematical calculations to large amounts of data automatically –over and over, faster and faster – is a relatively new development. Here are a few well-known ML applications that you may already be familiar with:

  • Tesla, the super-hyped self driving car

  • Netflix movie/TV show recommendations

  • Customer insights from Twitter

  • Fraud detection


What is the Difference between AI and Machine Learning?

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At first glance, AI and ML appear to have nearly identical definitions. After all, Merriam-Webster defines AI as "a branch of computer science concerned with the simulation of intelligent behavior in computers; a machine's ability to mimic intelligent human behavior." However, upon closer examination, it is clear that these two terms refer to completely different things.

AI encompasses a wide range of processes and practices, including neural networks and image processing. ML is one of these AI subsets. While AI can take many forms, such as self-driving cars or digital assistants like Siri or Alexa, ML describes a specific aspect of AI function, which is computers learning autonomously.


How do Computers Acquire Knowledge?

In simple words, a human user enters data into a computer, which then analyses the data and searches for patterns. When a computer discovers a pattern, it modifies how it processes or manages data to reflect what it has discovered. After identifying a sufficient number of patterns, the computer can begin to make predictions. In general, putting in more training data causes the computer to become more accurate and faster.

There are three main types of ML, namely supervised, unsupervised, and reinforcement learning.

Supervised Learning

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The training data is categorized or labelled with the "correct" outcome for this type of ML. The computer is then fed unlabeled data to process. It will compare the new and old data and then determine the outcome using the previous example. When the output is a real value, supervised learning is especially well-suited to categorizing items and regression.

Ever AI Technologies developed our very own license plate detector, which is part of our Ever AI project. Useful information such as the vehicle type, manufacturer, production year and road tax validity can be leveraged upon recognition. It applies object detection, by which the model is trained with suitable data to achieve its objective. This is the most common type of ML, and it is more reliable than unsupervised learning.

Unsupervised Learning

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The data in unsupervised learning is not labelled before being fed into the algorithm. After that, the machine attempts to find patterns in the data on its own. Unsupervised learning is especially effective at identifying similarities in groups and clustering them together, as well as detecting anomalies or abnormalities in data. Because there is no training data to compare it to, measuring the accuracy of an unsupervised learning algorithm is more difficult, but it can still provide valuable results and insights.

Reinforcement Learning