Machine learning is, without a doubt, one of the ideas that is setting the pace in terms of technological development, playing a decisive role in boosting process automation and improving workflows.
Machine learning: boosting the intelligence of computer systems
In other words, machine learning is a branch of artificial intelligence (AI) understood as the function of a program whereby it recognises patterns in large volumes of data, allowing it to make predictions.
In this way, through processing information, machines can become autonomous, learning by themselves, without the need for prior programming.
This allows the programme to learn, identify patterns and generate predictions by training the algorithm based on a database to be analysed. The idea is that, by repeating this process, the algorithms will be able to deliver results that are more and more reliable and accurate each time.
What is the purpose of machine learning?
Machine learning has become part of the everyday life of thousands of people and organisations. In fact, you may not notice it, but machine learning is involved in instances such as:
- When getting recommendations on platforms such as Spotify, Netflix or YouTube for playlists or content that a particular person might like.
- When viewing ads on social networks such as Instagram or Facebook about products or services that interest you.
- When using apps such as Waze to drive to a specific destination.
- Getting better results in Google.
- Optimisation of document management.
- When you receive emails, Gmail filters out emails that constitute spam.
Types of machine learning
Different types of machine learning can be identified depending on how machines learn to recognise patterns and make predictions. The most important of these are given below:
1. Supervised learning
Algorithms incorporate labelled data that holds 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 sorted based on tags, so the algorithms seek to recognise patterns in this disorganised data to gain new insights and group records by affinity.
In particular, this type of learning is of great value to companies when planning marketing campaigns as it works as an identifier of niche markets.
3. Reinforcement learning
When using this method, the algorithm learns based on experience, through trial and error exercises, rewarding successes and punishing mistakes.
The goal is that, with increased practice, the algorithms will be able to adequately predict the events being studied.
Main benefits of machine learning
There are several advantages that this field of artificial intelligence offers at the organisational level, among which the following stand out:
- Predicting market trends based on consumer behaviour, optimising marketing strategies and determining demand levels on the production of a good in a specific season.
- Optimising target market segmentation and ad targeting processes by identifying people’s consumption habits and preferences.
- Reducing the high number of security breaches by identifying anomalies that are often the result of attacks, e.g. malware. This is of the utmost importance at the moment, given that in 2021 there were 40,000 cyber-attacks happening daily in Spain.
- Improving relationships with customers by providing a closer and more personalised service. A great example of this are chatbots, tools to automate interaction with customers that have now reached enormous development levels.
- Encouraging 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. As such, its implementation in the business ecosystem is expected to continue growing.
Moreover, it is a key technology for boosting productivity and improving workflows at a general level, promoting the growth of organisations in an increasingly digital and dynamic environment, and is a determining factor in anticipating market variables.
