Machine learning is one of the trending technologies in business today. This type of artificial intelligence brings great benefits to companies. Its analytical capabilities represent a source of high-potential, automatic and autonomous knowledge. But what is machine learning, and how do machine learning algorithms work?
The fact that machine learning is one of the leading technologies in terms of investment comes to no surprise.
In fact, recent studies report that machine learning is among the most sought-after professional categories and a recent research by Algorithmia states that organisations have radically increased their investment in machine learning in 2021.
But why? What is machine learning's secret and why does is it so profitable for companies? Let's start at the beginning.
What is machine learning?
Machine learning is a form of artificial intelligence that uses mathematical algorithms to enable machines to learn in a similar way to humans and to perform analysis without being explicitly programmed to do so.
The aim of machine learning is to give machines the analytical capacity to solve problems on their own through identification, classification or prediction.
Although they are very similar technologies, it is important not to confuse machine learning and deep learning, which is, in fact, a variation of machine learning. If you want to learn the difference between machine learning and deep learning, you can read the article 'What Is the Difference Between Machine Learning and Deep Learning?'.
How do machine learning algorithms work?
The first time you hear about machine learning, you may feel like it is science fiction. Machines learning on their own may indeed seem like a futuristic prospect, but in this case, the future is already here.
Machine learning works through complex mathematical algorithms that have the ability to identify patterns in data sets. By identifying patterns, machine learning algorithms are capable of drawing conclusions from new data for which they have not been trained, applying similar patterns to those previously identified.
Through this powerful pattern recognition, machine learning algorithms are applied to a myriad of systems for predictive analysis or the generation of intelligent and automatic responses.
Machine learning has actually more to do with mathematics, statistics and mass tagging than with science fiction. Machine learning algorithms do pattern recognition in a similar way to what statistical formulas do. It all boils down to the analysis of huge amounts of data and the application of probability to calculate the most likely outcome for a given problem.
Machine learning: what is it used for?
Machine learning has countless applications. Although it may seem like a technology of the future, it is already part of our reality and we can find plenty machine learning examples in our daily lives.
Video or music streaming apps such as Netflix or Spotify use machine learning algorithms for personalised recommendations. Virtual assistants capable of answering questions asked by humans, such as Alexa or Siri, are probably the clearest example of machine learning. However, this technology is also used to optimise the search engines results such as Google, to operate robots or autonomous vehicles, to prevent diseases or to create antivirus software that detects malicious software.
Machine learning in business
In business, machine learning has become a crucial technology, mainly due to its predictive capabilities.
Predictive analytics are a highly valuable competency for a business because, among other things, they allows organisations to anticipate market trends, make predictions based on data, reduce risks, solve problems before they occur and make better decisions.
In addition to predictive analytics, machine learning algorithms are commonly used by companies to reduce the number of errors in operational and management systems, to strengthen data security, to increase the analytical capabilities of data analysis tools or for process automation.
- Find out how to implement a machine learning project here: 'How To Approach A Machine Learning Project'.
Types of machine learning
As artificial intelligence has evolved and machine learning has proliferated, the technology has evolved and different types of machine learning algorithms have emerged.
Machine learning is divided into two main models: supervised machine learning and unsupervised machine learning.
- Learn the difference between supervised and unsupervised machine learning in: 'The Differences Between Supervised and Unsupervised Machine Learning'.
Likewise, some consider deep learning to be a type of machine learning. However, deep learning has advanced so far that others now consider it to be a separate field of study.
On the other hand, the nature of machine learning projects can vary depending on how the machine learning algorithms are applied or used, which, through the necessary programming and coding, can be to virtually anything.
Although the hype around machine learning is growing, organisations need to understand that the potential of this technology is directly linked to the quality and performance of the algorithms programmed.
Much of what machine learning does is actually a statistical analysis of data which results depend on the logic of the analysis itself and, in this case, on the work of the programmers and developers, as well as the business logic implemented in the machine learning project.
Would you like to take advantage of machine learning's potential but have no idea where to start?