What is machine learning and how does it work? 

This market is expected to yield a CAGR (compound annual growth rate) of 38.8% between 2022 and 2029, a figure that gives us an idea of the importance of this technology for business development.

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Communication Team

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Machine learning is undoubtedly one of the concepts that is setting the pace in terms of technological development, being decisive in boosting the automation of processes and improving workflows. In other words, this technology, also known as machine learning, is one of the pillars of digital transformation.

Machine learning: boosting the intelligence of computer systems

In other words, machine learning is a branch of artificial intelligence (AI) understood as the ability of a programme to recognise patterns in large volumes of data, which allows them to make predictions.

In this way, by processing information, machines can work autonomously by learning on their own, without the need for prior programming.

This allows the programme to learn, identify patterns and generate predictions by training the algorithm from a database to analyse. The aim is that, by repeating this process, machine learning algorithms will increasingly deliver more reliable and accurate results.

What is machine learning for?

Machine learning has become part of the daily lives of thousands of people and organisations, whether we are aware of it or not.

Some of the instances in which machine learning is present are the following:

  • Receive recommendations on platforms such as Spotify, Netflix or YouTube for playlists or content that might appeal to a particular person.
  • Seeing advertisements on social networks such as Instagram or Facebook about products or services that are of interest to you. 
  • Using apps such as Waze to drive to a specific destination. 
  • Obtaining better results in Google searches.
  • Optimising document management.
  • When receiving emails, Gmail filters all those that represent spam.
  • In cybersecurity, new antivirus and malware detection engines can use machine learning to enhance scanning and speed up detection capabilities when recognising anomalies.
  • Natural language processing can be enhanced by this technology to simultaneously translate from one language to another, recognise the voice of different users or even be able to analyse sentiment.

Types of machine learning

Different machine learning models can be identified depending on how machines learn to handle pattern recognition and make predictions. Depending on the data available and the tasks to be addressed, four different types can be categorised: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.

The main features of these types are:

1. Supervised learning

Algorithms integrate labelled datasets containing prior information about what a computer is supposed to learn in order to make decisions and predictions. 

For example, an umbrella business can predict its level of sales by having recorded each day’s sales over the past years and the context in which they were made (month, temperature, weather, etc.).

2. Unsupervised learning

The database to be analysed has been ordered around tags, so the learning algorithms seek to recognise patterns in this disorganised data to obtain new knowledge and group records by affinity. 

In particular, this type of learning is of great value to companies when planning marketing campaigns by serving as an identifier of market niches.

3. Semi-supervised learning

As labelled datasets are complex, we come to the semi-supervised learning model, which, as the name suggests, has a bit of both of the models we have already discussed.

When using this approach, some of the comments are manually labelled. Once a small set of labelled comments is available, one or more supervised learning algorithms are trained on that portion of the labelled data and the resulting models are used to label the rest of the comments.

In a last phase, a supervised learning algorithm is trained by using as labels those already manually labelled and adding those generated by the previous models.

4. Reinforcement learning

Under this method, in order for the algorithm to learn by its own experience, it has to be based on trial and error exercises, rewarding successes and punishing failures. 

The aim is that, as the algorithms acquire more practice, they will be able to adequately predict the events under study.

Main benefits of machine learning

There are several advantages to this field of artificial intelligence, a technology that is getting closer and closer to the capabilities of the human brain, from an organisational point of view, among which the following stand out:

  • Predicting market trends based on consumer behaviour, optimising marketing strategies or determining the level of production demand for a good in a specific season.
  • Optimise targeting processes and advertisements by identifying people’s consumption habits and preferences.
  • Reducing the high number of security breaches by identifying anomalies that often occur in the face of attacks, such as malware. This is of utmost importance today, considering that during 2021 there were 40,000 cyber-attacks every day in Spain.
  • Improve customer relations by providing closer and more personalised attention. A great example of this is chatbots, tools for automating interaction with customers that have achieved enormous development today.
  • Encourage the commitment to innovation and the search for more effective technological solutions to solve failures and problems in organisations.

Machine learning offers multiple benefits for companies in various sectors, such as health, food, education, transport and advertising, among others. For this reason, its implementation in the business ecosystem is expected to continue to grow.

It is also a key technology for boosting productivity and improving workflows across the board, facilitating the growth of organisations in an increasingly digital environment.

What is the difference between machine learning and deep learning?

The difference between deep learning and machine learning, two of the main concepts related to AI, lies in different issues.

Within the field of data scientists, the truth is that both technologies seek to build systems that are capable of learning to solve problems without the intervention of a human, ranging from spelling prediction systems to others such as, for example, machine translation.

On the one hand, the level of human intervention. While machine learning requires a certain degree of human intervention in order to achieve the expected objectives, deep learning can achieve autonomy.

Regarding the level of complexity, machine learning systems are simpler and can run on conventional equipment, while deep learning systems require more powerful and robust software.

In terms of time and accuracy, the results between the two branches of AI are different. While it is true that the time for a machine to work autonomously in making predictions and identifying patterns is shorter in machine learning, the degree of precision offered by deep learning is greater.

Finally, another difference lies in the characteristics and organisation of the data. For machine learning to reach its full potential, it requires that the data be previously structured. As for deep learning, this technology can work with large volumes of unstructured data, which is of great value when it comes to identifying patterns or trends.

What are neural networks?

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms.

Both the name and structure of neural networks are inspired by the human brain, and mimic the way biological neurons signal each other.

Artificial neural networks are made up of layers of nodes, which are connected to each other.

These neural networks are based on training data with the aim of learning and improving their accuracy over time. However, when these learning algorithms have been fine-tuned, they are powerful and can be used as a basis for learning and improving their accuracy over time.

Conclusion

Within the framework of the digital transformation that we are experiencing in parallel to the technological revolution, numerous advances are changing our daily lives, often without us even being aware of it.

Machine learning, a branch of Artificial Intelligence, is precisely one of these technologies. A key concept in empowering the improvement of workflows and the automation of processes in a technology that is also known as machine learning.

Machine learning technology has a series of typologies depending on how machines learn to manage pattern recognition and make predictions. There are different types such as supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.

Among the main advantages offered by machine learning are predicting market trends, optimising target audience segmentation processes, reducing the number of security vulnerabilities, improving customer relations and encouraging innovation.


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