Machine Learning: What Is Machine Learning?

What Is Machine Learning?

The software or the Machines must be able to make decisions for themselves and react appropriately to unknown situations and work functionally according to them is known as Machine learning

In principle, machines, computers, and programs work only in the way they have been previously programmed, for example, if the order is established “in case of A, do B” the systems will always respond B to A. However, our expectations for modern computer systems are increasing and programmers cannot foresee all possible cases and dictate a solution accordingly. Therefore, it is necessary for the software to be able to make decisions for itself and react appropriately to unknown situations, for which algorithms are required that allow the program to learn, or what is the same, that the program be endowed in the first moment of data so that it can understand a second pattern and establish associations.

Around autonomous learning systems, related terms always arise that must be understood in order to have a clearer idea of ​​what machine learning means.

Artificial intelligence (AI) is an essential component in the digitalization process that society is changing so much in our era. What once seemed to be part of a science fiction film has been materializing in reality, so that today we talk with computers, we can find out from the mobile the shortest way to a gas station and our watches inform us of the activity physics we have done during the day. The technology is becoming smarter day, at the same time that researchers, programmers and computer scientists take on the role of teachers as they try to teach computer systems to learn for themselves.

Machine learning not only becomes important for researchers or IT companies such as Google or Microsoft but also plays a very important role in online marketing, which is being modified with the advancement of AI. In this text, we clarify how the already named AI has been developed in recent years, what is meant by machine learning, the existing methods of this machine learning and the reasons that drive marketing professionals to bet on self-learning systems.

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The History Of Machine Learning:

What Is Machine Learning

For centuries, ideas about robots and automatons began to take shape in the minds of human beings. The writers of romanticism already dealt with the topic of AI and, in fact, today we are still surprised by the robots that appear in movies, books, and video games, producing in us a feeling that oscillates between fear and fascination. However, it is not until the 50s of the last century that machine learning processes really begin to come true at a time when computers were very unknown and AI was part of fantasy rather than reality. In fact, although in the previous two centuries theorists such as Thomas Bayes, Adrien-Marie Legendre, and Pierre-Simon Laplace had already taken the first steps laying the foundations for subsequent investigations, it was not until Alan Turing’s work when the project of machines capable of learning materialized.

Google and Facebook also use machine learning to get to know their users better and offer them a greater variety of functions. For example, Facebook’s DeepFace is a facial image recognition program with a 97% degree of accuracy. At the same time, Google has clearly improved with Google Brain Project the voice recognition in Android operating systems, the search for photos with Google+ as well as the recommendations of videos on YouTube.

Different Machine Learning Methods:

Programmers differ mainly between supervised learning and unsupervised, whose algorithms differ greatly, although gradual intermediate levels are also noted. In supervised learning, examples are given to the system and the programmer establishes what value each data receives, for example, if it belongs to category A or B. Thereafter the self-learning system draws conclusions, recognizes patterns and, therefore, it can deal better with unknown data, always with the aim of progressively reducing the error rate.

A known example of supervised learning is the spam filter. Depending on the characteristics presented by the received message, the system decides whether it should appear in the inbox or if, on the contrary, it is stored directly in the spam tray. If the system makes a mistake, the user can readjust the features manually so that the filter will adapt its calculations in the future, allowing the software to achieve better and better results. This type of filtering program is based on Bayes’ theorem, of probability theory, and therefore is called Bayes filter.

On the other hand, in unsupervised learning, the figure of the “teacher” present in the supervised model and responsible for establishing relationships as well as giving feedback to the decisions that the system makes independently is suppressed. Instead, this program tries to recognize patterns by itself for what it uses, among others, to group analysis ( clustering ), consisting of selecting an element of all data, analyzing its characteristics and then comparing it with the elements already analyzed. . In case elements with the same values ​​have already been studied, the object of study is added to these but, if this is not the case, it is stored in isolation.

Systems that rely on unsupervised learning are carried out, among others, in neural networks. They are used, for example, to ensure network security, since they can recognize abnormal behaviors. Thus, if the system, when analyzing an element (which involves a cyberattack) is not able to associate it with any of the known groups, it recognizes the risk that this element entails and triggers the alarm.

But as already indicated, along with these two main systems there are also other levels: semi-supervised learning, effort learning ( reinforcement learning ) and active learning ( active learning ). These three methods belong to supervised learning and differ in the type and volume of user participation.

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Machine Learning: Its Role In Marketing

Machine learning

At present, machine learning plays an important role in the field of marketing, although companies generally use this advanced technology internally, especially in the case of Google. In fact, these systems are so new that they cannot be acquired as if they were solutions out of the box, ready to use them, but it is the large Internet providers that develop their own systems, driving progress in this field. This, together with the fact that there are also open source solutions promoted by independent researchers whose interest is not based on economic benefit, leads to increasing progress in this area.

Marketing, in addition to a creative part, is composed of analytical aspects. In fact, statistics on customer behavior (purchasing behavior, number of visits on a website, use of certain apps, etc.) play a fundamental role in deciding on certain advertising campaigns. Therefore, the greater the volume of data available, the greater the amount of information that can be derived from them. However, to be able to work with such a tangle of data, computer programs are required, where machine learning systems come into action since these machine learning programs can recognize patterns and consequently make well-founded forecasts, an activity for which people they are limited because they can hardly treat data objectively.

As a general rule, when an analyst works with the data, he has certain expectations about the values, inevitable preferences that often lead to misrepresentation of the results. In fact, the greater the volume of data that analysts work with, the more these differences are accentuated. However, although intelligent machines may also have certain prejudices, since unconsciously.

In summary, it can be affirmed that machine learning systems can influence four important points in online marketing:

The volume of information: those programs that work thanks to machine learning and have been well programmed allow to analyze a high volume of data and offer reliable forecasts so that marketing experts can obtain more accurate conclusions about the success or failure of certain campaigns and marketing measures.

Speed: the analyzes assume a considerable amount of time if they are intended to be done by hand, so the use of an autonomous learning system will reduce the analysis time, allowing at the same time to react more quickly to changes.

Automation: with machine learning, it is easier to mechanize complex processes and allows modern systems to adapt to new circumstances autonomously.

Personalization: These programs allow you to advise countless clients individually, by collecting and analyzing the data of each client. Individual recommendations and personalized customer journeys allow for much more effective marketing.

Also Read: Internet Of Things(IoT) And Its Revolution In Different Sectors

Other Fields Of Application For Machine Learning Systems:

Marketing is not the only area in which the application of machine learning grows progressively, but also has a presence in many other fields: it contributes to the progress of science, supports the advancement of technology, facilitates day today thanks to the electronic devices, etc. These are just some examples within the wide variety of fields of machine learning, which makes us assume that in the not so distant future this type of intelligent system will be present in all aspects of our lives.


In the field of science, machine learning is of equal or greater importance than in the field of marketing, since intelligent big data processing greatly facilitates the work of researchers, allowing, for example, particle physics to take a larger volume of measurement values, process them and thus determine the deviations. But also in medicine, it is very useful because nowadays doctors use artificial intelligence to make diagnoses and apply treatments, in addition to using machine learning to predict diabetes or heart attacks.


The omnipresence of robots is especially important in factories. These are necessary for mass production since they allow to automate different work processes, but in general, they have little relationship with self-learning systems, as they are programmed to perform a specific activity repetitively and without variations. However, if autonomous learning were introduced in these processes, machines could learn to master other tasks. But robots that integrate intelligent systems can also be useful in many other fields ranging from the space race to the domestic sphere.


One of the great challenges of machine learning is autonomous driving. Getting cars to be able to drive on their own and without causing accidents outside the test circuits can become a reality thanks to these intelligent systems. As it is not possible to program all situations, it is important to provide autonomous cars with machine learning systems, but autonomous driving is not the only field in which these systems have left their mark, given that intelligent algorithms in the form of neural networks they can analyze traffic and develop effective systems that, for example, regulate smart traffic lights, thus relieving traffic flow and preventing traffic jams.


On the Internet, intelligent learning is a very important piece. Previously the spam filter has already been named. With progressive learning, this program increasingly filters unwanted messages and makes spam disappear from the inbox. The same goes for smart programs that protect computer systems from viruses and malware more and more effectively. The search algorithms of search engines, especially RankBrain of Google, are also self – learning systems. Even when the algorithm does not know how to deal with a user’s search because it is the first time someone has done it, you can deduce what your query can be about.

Personal assistants:

In the private sphere of the home itself, the importance of these increasingly intelligent computer systems is increasing, transforming traditional homes into smart homes. Moley Robotics has developed, for example, an intelligent kitchen that with its mechanical arms can prepare meals. Also personal assistants such as Google Home and Amazon Echo, from which it is possible to manage the home itself, turn to machine learning to understand users in the best possible way. Also, many people carry assistants with them, since Siri, Cortana, and the Google assistant allow users to ask questions or send orders to the smartphone through voice commands.


Since the beginning of the studies on artificial intelligence, the ability of these programs to participate in games has caught the attention of researchers, which has been demonstrated in chess, checkers or Go, a game from China and probably the most complex game board in the world, facing machine learning systems and human beings. In the case of video games, developers turn to these machines to make their games more interesting. In addition, game designers can install this autonomous learning to create a game as balanced as possible between the computer and real players.

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