Know the Basic Concepts of Machine Learning Expense
Current technological advances do not reduce the possibility of machine learning expense that allows you to work more easily, especially in finance.
Machine learning itself is one form of application or implementation of artificial intelligence (artificial intelligence). The implementation has a focus on developing systems that allow for ‘self-study’ without having to be programmed repeatedly by humans.
In order for the machine learning expense to be independent, the material must be provided for ‘learning’ first before releasing the output. Even so, this independent machine cannot be fully capable of learning. This machine can only work or be applied to a particular focus, for example regarding finance.
By using machine learning, it allows you to work more easily. For example, you only need to take a picture of something and then the application or hardware will immediately recognize what you are shooting. After that, directly enter the data obtained or recognized into the database that is integrated with the machine learning application.
The Origin of Machine Learning
Before machine learning (ML) was discovered, AI (artificial intelligence) was discovered first. Then came ML which at that time the term was proclaimed Arthur Samuel (1959). In its development, ML did not work well let alone emerge a system of provability that is influenced by theoretical and practical problems of data representation and acquisition.
Then in the 1990s, ML re-developed and reorganized as a separate field from AI. Furthermore, the ML was created with the aim to overcome problems that are more practical. Because of this, ML is benefited now especially with the availability of digital information and the ability to distribute information and data via the internet.
The Role of Data in Machine Learning and Its Application
The thing to know and understand is that machine learning is nothing without data even though it is programmed as well and as sophisticated as possible. So that data is very important. As mentioned before, the data entered is for example.
Proverbial, when you have to do a math problem in a textbook, you will be easier to understand the problem through the examples that have been given previously. These examples will be imitated and because machine learning has AI capabilities that allow for self-study, from these examples, the application or hardware will recognize the data. It doesn’t even rule out the possibility that the machine will improvise.
The data scanned by machine learning will later be grouped into the ‘pool’ that has been provided. For example, for financial data, you can enter into Ms. Excel. Because of this, before the application with machine learning is used, it is necessary to have arrangements related to the ‘container’ that will be used.
Before widely used today in applications that can be installed on smartphones or on computers, ML has been widely applied, one of which is in the field of medicine. One way is to use an electrocardiogram record that is able to detect a person’s disease through the symptoms experienced such as heart disease.