What is Machine learning and what is it for?
Machine learning is a discipline that is becoming more and more noticeable. Those machines that have the ability to learn by themselves are attracting special interest from the general public, and it is not surprising, their particularities and the way they work are really fascinating. Its applications in the real world are notable and, despite the fact that in some cases we do not perceive these systems very directly, it is more than likely that we use some of its functions in our daily lives, such as the search engine Google . There are other more obvious applications, such as the ability of our smartphones to know faces, or interact with voice systems that try to imitate human communication.
Definition of machine learning
Machine learning or automatic learning is a branch of artificial intelligence that, through the initial development of a series of techniques, allows machines to learn on their own. Learning occurs when an intelligent agent is able to increase its performance and develop new skills through experience and the use of data available in its system. These data are collected as the agent experiences new events in their activities, the information of which is used by the system automatically to progressively improve their performance. The peculiarity is that these abilities that the agent gradually acquires were not initially registered in his computer core , but rather the development of these new abilities is based solely on the data that experience gives him.
Machine learning features
- Machine learning is closely related to pattern recognition.
- Machine learning is a field of data science.
- Machine learning algorithms learn autonomously.
- An intelligent agent is capable of predicting events based on historical data.
- A machine learning system is constantly improving over time.
- There are a wide variety of machine learning algorithms, but some are more widely used than others.
Objectives of machine learning: what is machine learning for?
Machine learning has a multitude of objectives that vary depending on the application sector. But in a generic way, we can conceptualize several points:
- Predict behaviors:Machine learning can analyze the historical data of an event and extract patterns of behavior that are repeated throughout said event. Having this information, the system can project situations that may occur in the near future.
- Establish data relationships:By analyzing various data that are related to each other, you can know what influence one data has on another. In this case, it is possible to determine the outcome of an event knowing the intervention that have certain data between them.
- Develop intelligent agents:Create intelligent agents to perform in a specific sector. Through automatic learning, its abilities are acquired by the data it collects in the events it experiences, creating a highly trained system to efficiently use its techniques.
- Analyze data in a massive way:Big data and machine learning can work hand in hand, and it works in the following way: Big data presents a huge amount of data in an orderly way, and machine learning studies this database in an advanced way to meet the objectives for which it is intended. For example, to more easily identify patterns and make better decisions.
Machine learning applications
Machine learning has various applications , here are some of them:
- Facial recognition systems.
- Development of bots in the area of videogames.
- Automatic speech recognition.
- Search engines.
- Medical diagnostics.
- Vehicular traffic prediction at a given hour.
- Understanding of texts.
- Anticipation of machinery failures.
- Artificial vision systems.
- robotics.
- Detection of malicious programs.
- Prediction of user behavior in social networks.
- Evaluation of the performance of the workers of a company.
- Fraud detection when using credit cards.
- Stock market.
