Text translations, shopping recommendations and offers, voice assistants, autonomous cars that see the road, or even the recommendation of series and films on streaming platforms. All these things have something in common, and that is the ability of these technologies to learn thanks to machine learning.
The ability to make systems capable of identifying patterns and analysing them in order to make a prediction has its origins in the last century. However, it is now when this discipline has been gaining more relevance thanks to the great availability of data that we are generating and the progress we are making with artificial intelligence applications.
In this context, Gartner experts define machine learning as a subset of AI that allows machines to develop problem-solving models by identifying patterns in data. They also explain that learning refers to the training process. That is, algorithms identify patterns in data and then, with the goal of providing more accurate results each time, use those patterns to adjust the model.
This technology seeks to mimic the way humans learn by gradually improving their accuracy.
It also allows computers to perform specific autonomous tasks without the need for programming. This learning capability is reflected in improved search engines, medical diagnostics and fraud detection in online payment mechanisms.
This technology is not only having a great importance in the present, but the tendency is also to continue improving its use in the future thanks to the great potential it has in the different processes within large, medium and small companies. But how does it really work?
Machine learning is made up of different types of learning models. Depending on the result we want to obtain and the nature of the data to be analysed, one of three types can be used: supervised, unsupervised or reinforcement. The algorithms of this type of learning are usually trained to classify, find patterns, predict results or make informed decisions.
Types of machine learning techniques
Supervised machine learning
Supervised learning consists of introducing a system of labels associated with the data analysed so that the system can detect patterns that allow it to make decisions or predictions.
This type of learning makes it possible to classify certain elements in everyday tasks such as detecting mail that we consider spam or grouping different images according to their category or theme in a search engine such as Google.
Unsupervised machine learning
In this case, algorithms do not identify patterns in previously labelled data, but are programmed to detect similarities in a specific type of information.
This is a possible solution for exploratory data analysis, such as extracting patterns from social media data to create highly targeted campaigns.
This occurs when algorithms are trained to learn by trial and error until they make the best decision in different situations by rewarding correct decisions. Currently, this type of learning is used to enable facial recognition or medical diagnosis.
Principal applications in companies and advantages
Machine learning can be really favourable in completely different sectors: medicine, engineering, bioengineering, robotics, linguistics, video games, web, human resources, big data, economics, finance, marketing, etc.
Nowadays, there are many applications that it can have and every day we are discovering fields where it can be of great use. Here are some applications and their advantages:
Optimisation of customer profiles. These data are used to know the profile of potential customers and in this way anticipate, predicting their wishes and needs.
Recommendation engines. Using past consumer behaviour data, algorithms can make complementary and relevant recommendations for customers during the shopping process in online shops.
Dynamic pricing. Through this technology, the price of each product or service can be predicted dynamically, taking into account data and economic variables of supply and demand.
Detection of financial fraud. Likewise, we can detect the risk of each customer, considering the existing probabilities of fraud.
Cybersecurity. Since the machine is constantly learning, it can know and distinguish abnormal behaviour patterns. Therefore, it is able to predict possible attacks.