Introduction to Machine Learning for SMEs

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Its main virtue is also its greatest disadvantage. Of all the applications and fields of study that digital transformation has generated , Machine Learning is the most utopian. However, the possibility of taking advantage of material autonomy is real, and many companies are already obtaining competitive values ​​from it.

However, what until a few years ago was an expensive and risky avant-garde, is now a standard made cheaper by the scalability of digital , which opens the doors for SMEs of all types and sizes to benefit from its advantages.

The Machine Learning market is expected to grow at an annual rate of 44.1% until 2022 , and that is why at MÁSMÓVIL Negocios we want to take a closer look at all the particularities that it presents in the day-to-day running of the smallest businesses.

Guide, tips and tricks for SMEs in Machine Learning
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What exactly is Machine Learning?
For science fiction, it is a robotic dystopia, the defeat of argentina number data humans by machines, and the loss of the conscious monopoly on the universe. Many companies continue to perceive this concept from this surrealist perspective , but the truth is that it is far from the real subject .

According to Expert Systems, it is " an application of Artificial Intelligence that provides systems with the ability to learn and improve autonomously through experience , without the need to be deliberately programmed ." It is thus closely linked to analytics and Big Data .

Machine learning has many uses , but every tool or application is based on data collection, processing in search of patterns, and making decisions accordingly. In other words, getting systems to learn by themselves and anticipate needs .

The virtues of this practical field respond, one by one, to the needs that companies have encountered in the new digital context. This type of learning offers SMEs the tool of defense against uncertainty, dynamism, and increasing competitiveness .



Types of learning
Although its application is flexible and impossible to homogenize, there are different levels of Machine Learning depending on the algorithms used by the tools. Their objectives are common, but the paths differ, understanding data processing from different perspectives.



Supervised learning
This is label -guided learning . In other words, filters that sort information based on different parameters or interests . The most obvious case of supervised learning is that carried out by Gmail with its different folders to distribute messages as they arrive, although they are also found in search engines that allow you to add filters.



Unsupervised learning
This is precisely the opposite case; Machine Learning does not use labels to function. Under this modality, algorithms operate arbitrarily, trying to find patterns among all the information they collect. Consulting firms that prepare reports on web traffic use this less circumspect learning.



Reinforcement learning
The algorithm works without any reference , in an unknown environment and through a trial and error logic. This system allows refining systems through accumulated and tested knowledge, although it takes longer to show results than previous models.



Main applications of Machine Learning
Due to its flexibility, machine learning is useful in almost any sector and industry. In a 2018 study, Statista outlined the main drivers that lead companies to adopt a machine learning solution , and the results are clear.



Business Analytics
A third of businesses interested in these applications see data as the main advantage. Given the growing volume of information generated year after year, Machine Learning represents a very valuable filter. With it, it is possible to process tons of inputs and thus refine efficient policies and decisions.

A local fruit and vegetable store, for example, that uses this technology can create data tables with its customers' consumption habits. This would open the door to predicting trends, saving costs in the supply chain, and increasing customer loyalty .



Security
One in four companies advocates Machine Learning to improve their security systems. The more traffic grows, the more threats grow, and although large multinationals have tons of resources to defend themselves, this context can mean failure for an SME.