Machine Learning And Its Applications
The translation of “machine learning” shows what this technique means. It encompasses the idea of machines having the ability to learn independently through large volumes of data. But how exactly do they do it?
Through big data and algorithms, identifying patterns of information and creating connections between them so that they learn to perform a task without needing the help of a human being and, of course, intelligently. These algorithms use statistical analysis to predict responses more accurately and deliver the best predictive result and the best: with less chance of error.
This technology can be divided into two main categories: supervised or unsupervised. Let’s explain better! Supervised algorithms are those where a person needs to interact by controlling the input and output of data, interfering with the training of the machine, and making comments about the accuracy of the predictions. Finally, the device applies what has been learned in its algorithm to the following analysis.
In the unsupervised category, algorithms use deep learning to process complex tasks without human training.
The possibilities of use are diverse, ranging from recommending series to finding evidence of cancer in mammograms. See some:
Computers can also be trained so that they can detect suspicious behavior in images that are captured by security cameras.
Companies like Boulder AI and IC Realtime already offer such products. In this way, it is possible to alert teams of guards to prevent occurrences, thus reducing dependence on human attention.
One of the most significant contributions this technique can offer is in the health area. With machine learning, it is possible to develop tools capable of diagnosing various types of diseases.
Fight Against Malware
According to data from Deep Instinct, most malicious software reuses code from previous versions. The similarity rate is usually between 90% and 98%.
Artificial intelligence can be trained to find patterns and identify the possible threats in the 2% to 10% that remain, mitigating the possibility of damages and losses.
Machine learning brings relevant advantages to the financial market. Software of this model receives training to detect trends in stock appreciation and thus ensure good business opportunities for investors.
In addition, artificial intelligence can learn how to assess customer credit, making a difference for finance companies, banks, and fintech.
Marketing And Service
Chatbots are increasingly present on service websites and online stores. Over time, the trend is for these robots to understand consumer demands much more accurately, helping to solve problems efficiently and quickly, reducing costs, and increasing customer satisfaction.
There are different possible uses for this technique when it comes to transport. GPS and map applications, for example, can significantly reduce the level of traffic on the road at a given time and day through a history of information and, thereby, provide better routes for users.
Your mobile device likely has a virtual assistant, whether Google Assistant or Siri. These technologies combine numerous machine learning capabilities, such as understanding natural language, recognizing the voice, and acquiring user habits. Artificial intelligence makes these technologies sound more human.
Machine learning techniques are highly versatile and can even help prevent fraud. PayPal, for example, uses such features to prevent its platform from being used for money laundering. Misuse of credit cards can also be identified with machine learning.
Search engines have been part of human life for more than ten years, and it is even easy to forget about their complexity.
Bing, Google, and other similar sites check which result you click on, whether or not you need to go to the second page of the list, and how long you stay on each site, among other factors. Therefore, they can evaluate websites, detect usage patterns and provide increasingly accurate answers.
Virtually every content site and online store have a system of recommendations for movies, products, music, and series. This software uses machine learning to observe the behavior of consumers and users and thus detect patterns. Netflix, for example, divides its viewers into more than 2,000 different groups, with suggestions tailored to each.