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Machine Learning (ML) is a modern innovation that has been improving both industry and professional processes, as well as our daily lives. It is a subset of Artificial Intelligence (AI) that focuses on using statistical techniques to build computer systems capable of learning from databases.
Applications of Machine Learning
One of the strong factors of machine learning is its ability to analyze data. Nowadays, it can handle any type of data and still operate in a multidimensional manner, processing and analyzing different types of data that common systems could not. This enables its application in different areas, creating more opportunities, as well as serving to improve relations between companies and consumers. It involves a group of algorithms that allow software systems to become more accurate in predicting outcomes. Machine learning in practice allows an industry to be more efficient, and can help predict various types of information, ranging from the medical field to public or cyber security. Here, we look at 10 practical examples of machine learning in different fields. Check it out!
10 practical examples of Machine Learning
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Healthcare and medical diagnostics
Machine learning can handle prognosis and diagnostic questions in the medical field and healthcare systems. Disease detection, patient monitoring, medical data analysis, and the management of inappropriate data are some of the practical examples of machine learning in this field. Thus, it can assist in diagnosing diseases, and many doctors use chatbots with speech recognition capabilities to discern patterns in their patients' symptoms. In practice, machine learning can help formulate a diagnosis, as well as recommend treatment options. In the fields of oncology and pathology, it is used to recognize cancerous tissue, for example.
Facial recognition
Machine learning can be applied to image recognition, of both objects like buildings or landscapes, as well as parts of the human body, like legs or hands. Furthermore, it also serves for facial recognition, which strengthens surveillance techniques used to track criminals and terrorists, making the locations where it is applied safer. Using a database, the system can identify certain aspects of the analysis and the image, matching them with faces.
Displacement and locomotion
Machine learning in platforms that use maps and routes guarantees punctuality through ML algorithms to calculate the fastest routes, which have less traffic, pointing out the arrival time, location, and the best route to a specific destination. Modern machine learning techniques have already incorporated deep learning models to analyze certain traffic, complex interactions between roads and their components, as well as elements of the surrounding environment. This helps prevent traffic jams, which improves safety, economy, and quality of life in a region. Emergency vehicles, such as ambulances, can find the shortest and fastest routes to reach the hospital, which can save lives, for example. In addition, people in general can save time in traffic, having a more productive day.
Public safety
Machine learning can improve the safety of a community by predicting, reducing, and responding to crimes. For example, 30 data scientists and machine learning engineers collaborated with an NGO, Safecity, to predict locations where sexual harassment occurred using maps that employed machine learning techniques.
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.
