What is machine learning? Understand the concept and know its types
Posted: Sat Dec 07, 2024 8:56 am
Machine learning is a technology that has been widely used by companies that want to improve their customers' experience and their managers' decision-making.
It is an area of computing that allows machines to access and interpret data, automating big data and artificial intelligence responses.
Machine learning can be defined as the ability of systems to analyze large volumes of data on their own, improving their performance when faced with specific problems ethiopia email address without requiring any type of human intervention.
That is, through artificial intelligence and machine learning, machines are able to identify data patterns, understand the connection between them, and perform tasks automatically.
The potential of this technology is practically immeasurable, since machine learning algorithms are fed with data and are capable of detecting patterns, interpreting them and not only solving problems, but offering answers by making deep and unimaginable predictions.
How do machine learning algorithms work?
In a more traditional and classic method, the programming structure creates new rules and establishes steps to provide a response after data analysis . This process requires, in most cases, the intervention of programmers and specialists.
Machine learning algorithms , on the other hand, can identify data, interpret it, learn from it, offer answers, create rules and connected questions, and make accurate predictions. All of this autonomously.
What are the types of machine learning?
Machine learning is divided into three main categories, which are:
Supervised learning
Supervised learning is a form of machine learning application based on prior knowledge. Through it, the system receives information that is already known and already has the correct answer.
That is, in this model both the questions and the answers are already connected and the function of the system is to show the solutions according to the variables.
An example of supervised learning is the spam detector, as it learns through email history, it can identify patterns and then filters the messages as spam or not.
Unsupervised learning
In this format there is no prior knowledge. Thus, the system is faced with a huge amount of data and cross-references it with the aim of finding patterns . This process is unpredictable and depends on a series of variables introduced into the system.
An example of this model is when a company wants to create loyalty campaigns for its customers. To do this, the system needs to analyze the behavior of its consumers, study their habits and group all the related information and detect patterns.
Reinforcement learning
This type of machine learning artificial intelligence teaches the computer to learn from its own experience and encompasses rewards and punishments .
This involves several trial and error tests. This helps the system learn to prioritize and understand what it needs to discard in order to make the right decision.
Self-driving cars are examples of this type of machine learning artificial intelligence, as they can assimilate the best routes, analyze scenarios and avoid accidents.
Machine Learning
What is deep learning?
In a simpler way, deep learning “trains” the computer and allows it to autonomously learn to recognize and identify patterns in various layers of the processing structure and thus be able to offer answers so that it can perform several tasks at the same time.
Deep learning is based on the concept of neural networks, which are a type of technology that attempts to simulate the functioning and behavior of the human brain.
Thus, deep learning big data allows the system to understand a high volume of information and offer immediate responses and results with this data.
What is the difference between deep learning and machine learning?
Deep learning is an evolutionary branch of machine learning, so to speak. This is because while machine learning is linear and facilitates a machine’s ability to learn, it also offers it the ability to develop and evolve as it learns and is exposed to complex data (big data), deep learning offers much more complex analysis and understanding.
It is an area of computing that allows machines to access and interpret data, automating big data and artificial intelligence responses.
Machine learning can be defined as the ability of systems to analyze large volumes of data on their own, improving their performance when faced with specific problems ethiopia email address without requiring any type of human intervention.
That is, through artificial intelligence and machine learning, machines are able to identify data patterns, understand the connection between them, and perform tasks automatically.
The potential of this technology is practically immeasurable, since machine learning algorithms are fed with data and are capable of detecting patterns, interpreting them and not only solving problems, but offering answers by making deep and unimaginable predictions.
How do machine learning algorithms work?
In a more traditional and classic method, the programming structure creates new rules and establishes steps to provide a response after data analysis . This process requires, in most cases, the intervention of programmers and specialists.
Machine learning algorithms , on the other hand, can identify data, interpret it, learn from it, offer answers, create rules and connected questions, and make accurate predictions. All of this autonomously.
What are the types of machine learning?
Machine learning is divided into three main categories, which are:
Supervised learning
Supervised learning is a form of machine learning application based on prior knowledge. Through it, the system receives information that is already known and already has the correct answer.
That is, in this model both the questions and the answers are already connected and the function of the system is to show the solutions according to the variables.
An example of supervised learning is the spam detector, as it learns through email history, it can identify patterns and then filters the messages as spam or not.
Unsupervised learning
In this format there is no prior knowledge. Thus, the system is faced with a huge amount of data and cross-references it with the aim of finding patterns . This process is unpredictable and depends on a series of variables introduced into the system.
An example of this model is when a company wants to create loyalty campaigns for its customers. To do this, the system needs to analyze the behavior of its consumers, study their habits and group all the related information and detect patterns.
Reinforcement learning
This type of machine learning artificial intelligence teaches the computer to learn from its own experience and encompasses rewards and punishments .
This involves several trial and error tests. This helps the system learn to prioritize and understand what it needs to discard in order to make the right decision.
Self-driving cars are examples of this type of machine learning artificial intelligence, as they can assimilate the best routes, analyze scenarios and avoid accidents.
Machine Learning
What is deep learning?
In a simpler way, deep learning “trains” the computer and allows it to autonomously learn to recognize and identify patterns in various layers of the processing structure and thus be able to offer answers so that it can perform several tasks at the same time.
Deep learning is based on the concept of neural networks, which are a type of technology that attempts to simulate the functioning and behavior of the human brain.
Thus, deep learning big data allows the system to understand a high volume of information and offer immediate responses and results with this data.
What is the difference between deep learning and machine learning?
Deep learning is an evolutionary branch of machine learning, so to speak. This is because while machine learning is linear and facilitates a machine’s ability to learn, it also offers it the ability to develop and evolve as it learns and is exposed to complex data (big data), deep learning offers much more complex analysis and understanding.