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Machine learning is a field of study with algorithms and techniques that are revolutionary, being increasingly present in the modern world. Essential for the advancement of technologies and the way in which various services and products are developed, from scientific research to industry, machine learning is a product of artificial intelligence, which has revolutionized (and is revolutionizing) the paradigms of the 21st century. Pix Force not only recognizes the importance of machine learning, but also applies it in its products, aiming to solve various industrial problems and needs. But what is machine learning? How does it work and what methods does it present? That is what we are going to explain to you here. Happy reading! ALSO READ: • Learn all about Computer Vision
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What is the meaning of machine learning?
We can translate the meaning of machine learning as “aprendizado de máquina”, which serves as a base concept to understand how it works. It is a branch of artificial intelligence (AI) and computer science that focuses on the use of data and algorithms to imitate the way humans learn, gradually improving its accuracy. Hence the term “learning”, from the English verb “to learn”. In this case, machine learning is a vital component in the field of data science, and its contributions are significant and central to this area. Through the use of statistical methods, algorithms are trained to make classifications and predictions. This is important for data mining, which is basically an automated process of processing data that we cannot see with the naked eye. Data mining looks for correlations, anomalies, and patterns in large data sets, with the aim of predicting outcomes. With the trend of ever-increasing data, the market demand for data scientists also grows. Similarly, technologies with machine learning are expected to present themselves in the market, since they are the ones that will have the tools to solve diverse industry problems — something that Pix Force does in its daily routine in the production of technology involving computer vision.
How does machine learning work?
The basic concept is to use statistical learning and optimization methods to enable a computer to analyze databases, identifying patterns. Thus, machine learning techniques perform an exploratory data analysis to identify recurring trends. This, in turn, provides useful information for future projects, models, and processes involving machine learning. The process can be divided into three parts: • Decision process: this is the use of algorithms present in machine learning to make classifications or predictions. Thus, based on input data, whether labeled or not, the algorithm is able to produce an estimate about the patterns seen in the data.
• Error function: this function serves to evaluate the model's prediction capacity. If there are other examples, the error function can make comparisons, thereby discovering the accuracy of the model in question. For example, whether the decision process understood the information correctly.
• Model optimization process: if the decision process makes mistakes, model optimization serves to solve them. Thus, the algorithm analyzes where the errors happened, and then updates how the decision process is carried out. In this way, the next errors will be less significant.
In other words, the machine learns by trial and error, mimicking what happens to us. For example, it guesses the answer, sees how much it got wrong, and then adapts and tries again! In this way, machine learning can be applied to different types of products or services, such as: • In the financial system, data can help identify investment trends to contribute to investor decision-making;
• In the government and corporate sectors, machine learning helps identify ways to save costs;
• In commerce, machine learning can be applied to help companies better understand their customers and personalize their products;
• Fraud prevention: machine learning can help organizations combat fraud losses by using data and performing analyses.
Check out more examples and applications of machine learning in this text.
What are the machine learning methods?
There are three primary categories of "machine learning": supervised machine learning, unsupervised machine learning, and semi-supervised machine learning. Let's analyze each of these concepts.
Supervised machine learning
This type makes use of labeled (or classified) datasets to train algorithms in classifying data or predicting outcomes with accuracy. The data it uses is already understood to some extent, and within a set that has already been established. As data is fed into the model, it adjusts itself until the information fits correctly into the process. This occurs as part of the cross-validation process to avoid issues of underfitting or overfitting. In this way, supervised machine learning is present in various human solutions for everyday problems, such as sorting spam messages into a separate folder, away from the rest. In addition, it is used in neural networks, logistic regression, linear regression, among other areas.
Unsupervised machine learning
In this case, algorithms are used to analyze and cluster data that has not yet been placed into some type of set or classification. The algorithms then discover hidden patterns or data groupings without the need for human intervention. Its ability to uncover similarities and differences in the information it finds makes it the ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, as well as pattern and image recognition. This type is also used to reduce the number of features in a model, through the process of dimensionality reduction. Other algorithms used in unsupervised machine learning include neural networks, probabilistic clustering methods, among others.
Semi-supervised machine learning
As the name suggests, it is a middle ground between the other two. During training, it uses a small portion of already labeled data to guide its classification process, with the rest of the composition being extracted from a larger, unlabeled dataset. This type of machine learning can solve the problem of not having a sufficient amount of already classified data to train a supervised learning algorithm.
What is machine learning used for and what is its importance?
Both machine learning and data mining are crucial tools in the process of better understanding information from large databases, something increasingly present in companies, industries, and research areas. And this is mainly due to two basic reasons: • The scale of data: companies find themselves facing a massive volume of data, which varies among itself, and which needs processing. Models that can be programmed to process this data on their own, determine conclusions, and identify patterns, therefore, are invaluable.
• Unexpected discoveries: since machine learning algorithms update themselves autonomously, analytical accuracy improves every time they perform processing. This is because it teaches itself according to the database it is analyzing.
Therefore, one of the main functions of machine learning is to go beyond data collection. This technology is characterized by production and efficiency in the use of acquired data, so that the analysis itself is done with less human intervention. This means that the machine intelligence itself allows complex and larger data to be processed and analyzed alongside the desired results. Among the possibilities, it is possible to: • Determine customer trends
• Detect problems and fraud
• Analyze buying trends and other key objectives.
In this way, when machine learning is applied to the business and industry world, the benefits are many, since it is possible to make better use of data. Furthermore, machine learning helps your company keep up with market trends, maximizing business opportunities. In view of this, Pix Force recognizes the importance of machine learning for companies and industries, keeping this in mind when creating products that can solve potential problems. What did you think of this text? Interested in the subject? Pix Force wants to help you. Get in touch with us! Oh, and if you want an explanation to help you understand what machine learning is even better, check out this video below: https://www.youtube.com/watch?v=1_c_MA1F-vU
Fabio Caraça
Fábio Caraça is the Chief Growth Officer at Pix Force. He leads Pix Force's transformation into a scalable SaaS operation, combining strategic vision, culture, and high-impact execution.
