What is machine learning?

Machine learning is a subset of Artificial Intelligence (AI) with the most applications. Students can learn how to use this tool and take advantage of algorithms to optimise work in banks, online stores, social networks, and any other type of digital environment.

If you want to learn more about how to apply machine learning in different contexts, you can study the Degree in Computer Engineering offered in Madrid at Universidad Europea, where you will investigate how to improve the efficiency of technology in computers and tablets, among other things.

Definition of machine learning

Machine learning is an AI utility that allows computers to optimise themselves with neural networks to recognise patterns and improve their interaction with them. In other words, it involves developing intelligences that learn autonomously through observation and working with the data they regularly operate with.

An expert in Artificial Intelligence is trained to help machines predict new patterns and information through a series of adjustments and data extracted from the AI's own previous records. As a result, these tools are becoming increasingly efficient and fast, and their margin of error is reduced when performing the tasks for which they were designed.

Types of machine learning processes

There are currently four general formulas for working with machine learning: supervised, unsupervised, semi-supervised and reinforcement.

• Supervised learning: the computer has a group of pre-labelled data with which it can perform a human task. The learning model is similar to that used by humans naturally.
• Unsupervised learning: unsupervised learning subjects the machine to a source of data that has not been previously classified. It is the AI that has to extract information from it and establish patterns to then apply them in the development of the tasks required of it.
• Semi-supervised learning: there is only a partial set of previously catalogued data. AI performs its machine learning based on this data and applies these protocols to the unlabelled data.
• Reinforcement learning: once its basic machine learning has been developed, the PC continues to study its environment to minimise risks and improve its responsiveness. The computer must be given a reinforcement signal so that it can initiate actions.

Machine learning processes

Machine learning is one of the most complex applications of AI in terms of its development. To make it happen, AI experts work with two main tools: neural networks and deep learning.

Neural networks

A neural network is a computing model based on the form and functioning of the human brain.

Our intelligence and learning are made possible by a network of interconnected neurons that share information extracted from the outside world in different layers or levels, allowing us to process and integrate it to produce knowledge.

AI neural networks faithfully mimic this process. To do this, graduates of the Degree in Artificial Intelligence generate interconnected artificial neurons that receive stimuli (called inputs), compute them and generate a result.

This information is transmitted to other layers of neurons so that they can learn and improve protocols based on the data obtained.

Deep learning

The concept of deep learning is directly related to that of machine learning. It is, in fact, a very specific branch of machine learning that does not require human participation for AI to continue improving.

With this tool, Artificial Intelligence uses its own neural network to continue testing and improving tasks autonomously. Thanks to this, machine learning never stops and machines offer users increasingly optimised and convenient operation.

How machine learning works and examples

The imitation of the human learning process by AI-enabled machines can be divided into five clearly distinct phases.

• Data collection

Initially, AI accumulates all possible data to generate patterns and perform machine learning through analysis. In the business world, this involves information about users, common customer queries, comments made by internet users, or trends in the registration of potential buyers on the website.

• Design of guidelines

AI determines which information is accurate and can be used to establish subsequent working guidelines. This is done by highlighting the most frequently repeated data sets to facilitate and automate certain tasks. A direct application would be the study of purchase histories, product access points or errors reported by customers.

• Establishing training

Specialists study the data and choose the machine learning model so that the PC can optimise its response to the task it has been assigned. In this regard, several methods can be chosen, such as decision trees, support vector machines or, most commonly, neural networks. Once the learning formula has been implemented, the AI is trained according to the models already indicated (supervised learning, unsupervised learning, etc.).

• Evaluation of results

Engineers specialising in AI assess the response that artificial intelligence is giving after analysing the information and going through its training phase. To evaluate the effectiveness of the process, there are performance metrics and techniques such as cross-validation or retention validation.

• Implementation

If the entire process has met expectations, the AI is considered to have passed its machine learning phase and is implemented in the day-to-day running of the company as a fundamental support for its activities.

Successful examples of incorporating AI with machine learning

Many companies today rely on the efficiency of AI machine learning to satisfy their customers. Here are some of the most prominent examples:

• Netflix and Spotify: both the audiovisual content platform and the music streaming service use AI to make personalised recommendations to users based on their tastes. To do this, machine learning focuses on collaborative filtering and natural language processing models to understand how the catalogues of both companies are rated online.
• ChatBot: automated chat is present in a large number of companies. From online shops to banks, they all have this small dialogue box on their websites to filter queries and direct each customer to the technician they need. In many cases, AI is able to resolve the query directly without the need for human assistance.
• AirBnb: the holiday rental company allows its hosts to activate the Smart Pricing feature, which means that the cost of a night's accommodation varies automatically based on real-time demand.

These are just a few examples of what machine learning can achieve for businesses. It is a tool of the future that is already being used by professionals with extensive knowledge in training AI.

Machine learning is revolutionising the way we interact with technology, making systems smarter, faster, and more responsive across countless industries. At Universidad Europea, students can gain the skills needed to thrive in this evolving field through a variety of engineering degrees. 

Whether you're interested in Computer Engineering, Artificial Intelligence, Telecommunications, or Industrial Engineering, UEM provides the knowledge and hands-on experience to explore the full potential of machine learning and its real-world applications. By studying one of these innovative programs, you'll be prepared to lead the future of AI development and technological advancement.