Machine Learning algorithms, what are they and in what areas are they used

After a challenging 2022, which saw a drastic 46% decline, the machine learning market reached a value of $158.8 billion in 2023, highlighting a 120% growth driven by the boom in demand for artificial intelligence-based solutions across various sectors.
Machine Learning, or automatic learning, is a branch of artificial intelligence and thus computer science, which enables an intelligent machine to improve its performance over time through the acquisition of new data and by analyzing the results obtained from previous choices.

The first major figure associated with machine learning is Alan Turing, considered the father of Artificial Intelligence, who first hypothesized the need for specific algorithms to create machines capable of learning. Technological evolution in this field now allows us to have various modes of automatic learning, distinguished by the algorithms they are based on and the goals they aim to achieve.

Based on the type of algorithm used and therefore the way the machine learns and accumulates data, three main systems of machine learning can be identified:

1. Supervised Learning: involves providing the intelligent machine with a set of specific concepts, models, and examples that serve as a base of information to provide the best response to each question to be answered. Algorithms using supervised learning are used in many sectors, from medical to voice recognition, and they have the ability to make inductive hypotheses, i.e., hypotheses obtained by scanning a series of specific problems to obtain a solution suitable for a general problem.
   Unsupervised learning is characterized by the fact that the machine uses the provided data without having examples of their use and therefore without having any knowledge of the expected results. In this case, unlike the previous one, the machine is required to collect the received information, organize it, and deduce its usefulness.
2. Unsupervised Learning: is characterized by the fact that the machine uses the provided data without having examples of their use and therefore without having any knowledge of the expected results. In this case, unlike the previous one, the machine is required to collect the received information, organize it, and deduce its usefulness.
3. Reinforcement Learning: is characterized by the fact that the machine is equipped with systems and tools capable of understanding the characteristics of the surrounding environment and improving its own learning. In such cases, external support elements are provided to the machine to make learning more effective. An example of this is self-driving cars, which, thanks to a complex system of supporting sensors, can navigate city streets and beyond, recognizing obstacles, following road signs, and much more.

The fields of application of machine learning algorithms are numerous. For example, in the energy sector, automatic learning algorithms can be used to find new energy resources, analyze minerals in the soil, predict failures in sensors used in refineries, and make oil distribution more streamlined and efficient.
In the sales and marketing sector, machine learning can be used to store, analyze, and utilize consumer purchase data to personalize the shopping experience and target marketing campaigns, leveraging the interests revealed by each user and their past purchases.
Finally, banks and other companies in the financial industry use machine learning technologies for two main purposes: identifying important information in data and preventing fraud. Information can identify investment opportunities and help investors know when to act. Meanwhile, data mining can identify customers with high-risk profiles or use computer surveillance to report possible fraud alerts.

The most important aspect of Machine Learning is iteration. Thus, the more models are exposed to data, the more they are able to adapt autonomously. Thanks to new processing technologies, today's machine learning is not the Machine Learning of the past. This science is not new, but it is gaining new momentum. Although many machine learning algorithms have been in circulation for some time, what enhances their effectiveness today compared to the past is the computing power of machines and the volume of data available to train models.