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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.
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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
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.
Agriculture
In agriculture, machine learning enables more precise cultivation methods, with less labor, and high quality in production. Furthermore, it also provides insights and recommendations regarding crops, thus allowing farmers to minimize their losses. There are some applications that use machine learning to make crop yield predictions, which increases the food security of the region.
Smart Assistants
Siri, Alexa, and Google assistants are some of the smart assistants present in our daily lives today, demonstrating practical examples of machine learning, helping us perform activities by setting reminders, alarms, checking the weather, etc. Voice-recognition smart assistants have several benefits, such as making people with some type of disability more independent. Additionally, they can serve to alleviate the feeling of loneliness for people living alone.
Government Industry and Policymaking
The use of machine learning helps authorities track and manage immense amounts of data generated by public surveillance devices. Real-time data analysis, which serves to detect anomalies and threats, allows law enforcement agents to track criminals and lost children. Furthermore, internet service providers can also be more successful in identifying instances of suspicious online activities linked to child exploitation. In a practical example, there was an occasion where a team of scientists and ML engineers applied machine learning to improve public sector transparency by allowing greater access to government contracting opportunities.
Workplace Safety
ML applications improve workplace safety by reducing accidents, helping companies detect potentially sick employees as soon as they arrive, and helping organizations cope with natural disasters.
Environmental Protection
Machine learning algorithms can help promote environmental sustainability. A good example is IBM's Green Horizon Project, where environmental statistics from sensors are used to produce pollution forecasts. The goal, in this case, is to reduce the environmental impact.
Cybersecurity
Platforms like PayPal and GPay use machine learning to track transactions and differentiate between those that are legitimate and those that are not. In this way, machine learning maximizes cybersecurity, preventing online monetary fraud.
Conclusion
Machine learning is here to stay, and it has several practical applications, serving in various human areas of modern life, from health and safety to common daily tasks. With the automation of activities, data analysis, and accuracy of results, ML makes our lives easier, faster, and safer, providing several advantages that accompany the advancement of modern technologies. With the refinement of machine learning and future research and development, its practical use in these areas tends to become more effective, benefiting human societies in general. Want to know how Pix Force can help your company use ML to optimize processes and improve its results? Get in touch with us!
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.
