Machine learning. A term that you have often heard, but may still feel foreign. By learning machine learning synthesis ai, you will not only increase your understanding of new terms that are currently trending but also learn about one of the important technologies that we often find useful nowadays. Machine learning is a data analysis method that allows the system to study data independently. Either with little help or even no human intervention at all. In other words, now machines can not only work automatically but can also learn with the datasets for machine learning to make their performance better from time to time.
As part of artificial intelligence, no wonder this technology can make machines more “smart”. This intelligence is obtained from analyzing data patterns. Like humans, the more often you do math problems, the more you will understand the right formula pattern to solve the problem, right? Likewise with machine learning. So, the continuous pattern identification process can help the system predict the results of the analysis being carried out.
Everyone knows that in the digital age, the amount of data is piling up. In 2020 alone, the average data generated per person per second is 1.7 MB. Imagine, in seconds! So, it can be concluded that the longer it is, the more data is available. Of course, this is a good opportunity because data can provide many benefits. The problem is, how to process so much data so that it can become useful information. Well, this is a problem that machine learning can solve. This is because the increased volume and variance of data can be analyzed more efficiently using this method.
Let’s take an example. Suppose you work in the investment and banking sectors. You can analyze customer data one by one to find out their risk profile. So, the analysis process is done manually. You can use machine learning to analyze all customer data. Later, the system will tell which customers are eligible for a loan based on their risk profile. The second option is the better choice. Because besides the analysis does not need to take a long time, the results can also be more accurate because they are based on objective historical data.