Machine learning has a number of advantages that can be applied to different specific practical cases. Let’s look at some applications of this branch of Artificial Intelligence.
Email management through machine learning is carried out within supervised learning – one of the types of machine learning – in which an algorithm detects and classifies emails based on previous emails (with data such as the subject, the sender, the body of the email or audiovisual material).
Similarly, it is also capable of cataloguing those emails likely to be spam or, in another vein, responses can be personalised depending on the category.
Machine learning does not only affect the perspective of the person receiving the email. It is also useful for those responsible for sending them to design and personalise the content – including the design or visual elements that can be incorporated – thus improving both the performance and the interaction of the campaigns.
Transport
Machine learning can help the transport sector in different ways.
By analysing data on traffic volumes, historical weather conditions or delivery patterns, the routes that transporters must travel can be optimised, increasing efficiency and reducing times. Fuel costs are also reduced by optimising journeys.
Machine learning also helps with fleet management as it can anticipate possible mechanical failures and proactively schedule maintenance. This reduction in costs also results in an increase in productivity.
These issues that we have discussed serve to improve the conditions in which we operate, which can lead to better customer service.
Social networks
Social networks have evolved since their inception at the end of the 20th century, and as well as being leisure or information tools for users, they are also of great relevance to the marketing sector.
In this case, machine learning processes and collects user data so that algorithms are able to learn about their behaviour and thus obtain information of interest to them.
Thanks to its predictive analyses, it can anticipate behaviour and make suggestions, which means that the world of advertising can fine-tune its content by limiting the information it offers to users. This information focuses on people’s behaviour, but without identifying them individually.
Health
In the healthcare sector, machine learning can be very useful due to the large amount of medical data that can be accumulated.
Although we are dealing with a technology that can bring advantages to the healthcare sector (support in areas with fewer resources, with a lack of healthcare personnel or optimisation of limited resources), it also presents a series of challenges in a critical area.
Delicate issues such as data privacy, data interpretability or the presence of potential biases are some of the challenges facing machine learning applied to the world of healthcare.
Apart from these delicate issues that may arise, machine learning can be used in different areas such as early disease detection, radiological analysis, genetic research or drug discovery and manufacturing.
Banking and finance
Offering better services and products is one of the advantages of machine learning in the banking sector, but not the only one.
Improved data sources, together with the analytical capabilities based on machine learning models that combine them, allow them to learn and recalibrate, enabling each customer to be treated individually according to their specific financial history, as stated by BBVA on its corporate blog (in its Spanish version).
This technology can also be used to detect potential fraud by recognising suspicious or atypical transactions that may require further investigation.
