Machine learning is an AI discipline in which machines learn from data to support decision-making processes or make predictions without being programmed to do so initially.

Interest from organisations has grown over the last few years, partly due to the rise of big data and the significant increase in the volume of data.

This article will explain exactly what machine learning is and how it works.

Understanding the Basics of Machine Learning

As mentioned above, machine learning is an AI discipline that enables machines, through algorithms, to learn from existing labelled data.

In modern industry, machine learning is widely used in sectors where analysis and prediction from the core of organisational strategy, such as finance and healthcare research.

For example, in order to identify whether a picture contains a cat, machine learning is trained by analysing a large set of pictures of cats. Then, when presented with an unlabelled picture, it will be able to correctly identify whether the animal is a cat.

Machine learning works similarly to humans in that we don't give it explicit rules; it detects statistical patterns in the data and gains the capability to detect unlabelled data.

The above paragraph is an example of supervised learning. There are indeed multiple forms of machine learning.

How Machines Learn: Understanding Different Learning Methods

Firstly, supervised learning is a category of machine learning that uses labelled datasets to identify patterns with human guidance. Once trained, the model can use this knowledge to accurately predict outcomes or classify new, unlabelled data. Classifying emails as spam or not spam is a perfect example of supervised learning.

In contrast, unsupervised learning involves algorithms that are trained to identify patterns independently. The algorithm discovers patterns independently in order to uncover hidden structures, clusters or representations of the data. A common use case of unsupervised learning is finding a relationship between two items purchased together.

Semi-supervised learning involves training algorithms using a small amount of labelled data and a large amount of unlabelled data. This approach combines the strengths of supervised and unsupervised learning, as labelling data is costly and time-consuming, whereas unsupervised learning is inexpensive.

Reinforcement learning is quite different from the previous three approaches. Instead: The core idea is learning through trial and error. An agent is defined as anything that can perceive its environment, make decisions and take actions to achieve a goal. Agents learn to make decisions by interacting with an environment and receiving rewards or penalties.

This maximises performance efficiency over time. Large Language Models (LLMs), which are used by Generative AI, for example, improve their efficiency through human feedback.

Advancing AI: Deep Learning, Autonomy, and Emerging Challenges

In conclusion, machine learning allows organisations to solve problems using supervised or unsupervised learning methods. These methods provide powerful tools for analysing and exploring large and growing datasets.

Beyond these classical forms, another type of machine learning is emerging: deep learning.

This remarkable development relies on neural networks with multiple abstraction layers. It can extract highly sophisticated data from images, sounds and text. This makes complex tasks such as image recognition and automated translation possible.

In the future, we may see machine learning applied to new use cases alongside new techniques and methods. In the era of agentic AI, systems will be increasingly capable of reasoning, planning, and acting autonomously across complex environments.

This shift will enable models to not only make predictions, but also take the initiative, coordinate tasks and adapt dynamically to new situations.

However, this evolution also brings significant challenges. It will be essential to ensure the safety, transparency, and robustness of agentic AI systems, especially as they gain more autonomy and decision-making power. Issues such as data bias, model interpretability, privacy protection and resistance to adversarial attacks will become critical concerns.

Furthermore, the environmental cost of training large models and the ethical implications of delegating decisions to AI systems will necessitate careful governance.