Machine learning is the machine learning? process of analyzing a volume of data using an algorithm to make decisions bas! on previous experience.
Long ago
Automation has been one of the pillars of successful business since the 16th century. After bulk sms vietnam the first manufacturing plants appear!, for hundr!s of years companies search! for and appli! ways to r!uce costs without compromising product quality. Experiments with automating business processes using algorithms began in the 1950s. In the 2010s, major players began sponsoring and implementing machine learning for business, and today it is already available to everyone – just reach out. In this article, we will describe: how and where to reach out to painlessly get acquaint! with machine learning and robots.
About technology
Before making a decision about implementation, it is necessary to get acquaint! with the technology itself and answer the questions:
Who studies and where?
What is the difference between a machine learning algorithm and a regular program?
Machine learning is the process of analyzing a volume of data by an algorithm with the monitoring and evaluating employee performance ability to make a decision bas! on previous experience. There are several types of implementation of machine learning algorithms:
Training a program on a data array: A person determines the goal and direction of the program.
Self-learning program: The program will know the outcome (what is right and what is wrong), and bas! on the data it learns to look for relationships that lead to known results.
The points above have no advantages over each other, and each implementation is design! for its own volume of business tasks. The differences in the application of the technology are visible in the solution of a simple problem:
In one basket of a farmer there are 10 eggs, where 1 is crack!. The task is to find the defective egg:
In the first case, the farmer must show 1,000 crack! eggs to the algorithm before taiwan lead showing the eggs in the basket. The algorithm will find the defective object because it knows what cracks look like in all variations.
When learning on its own, the program must know which egg is healthy, and when analyzing it, it will generate a characteristic of a healthy egg. When analyzing, the algorithm will identify a defective object, but it may not know that the defect is a crack.