Technology on computers continues to grow rapidly until now, where computer machines can do something to make human work easier. At the beginning of the computer was invented, the machine got a number of commands to do something that humans wanted in accordance with the rules and regulations. Finally, in 1822 ago, a mathematician and the founder of the first computer, Charles Babbage, created a machine that could perform arithmetic operations.
The development of this method continues to grow and become a computer that until now we use everyday in work or help with many things. Computer development will always develop, especially on artificial intelligence or Machine Learning (ML) and AI (Artificial Intelligence) technology.
There is a difference between Machine Learning (ML) and Artificial Intelligence (AI). Artificial Intelligence (AI) refers to computer programming to make rational decisions, while Machine Learning (ML) focuses on the relationship between data variables with a mathematical approach. Machine Learning is a mathematical model formed in a data pattern, which is commonly called a dataset. While AI will perform tasks that are done using artificial intelligence. Where we need to make the machine can work alone based on human past experience.
This method is quite influential in the industrial world and some of the industry is currently working using the Machine Learning method. What is the industry using all of this for? In our opinion, the goal is clear, namely to make work or the production process faster.
1. What is Machine Learning
Broadly speaking, Machine Learning is a system development that can work repeatedly without human assistance. The machine can read various data, analyze the data and determine the data.
Machine Learning is an approach method to the type of artificial intelligence Artificial Intelligence (AI). Artificial Intelligence (AI) can program machines to remember and make decisions like we humans and can learn various kinds of data through artificial intelligence (AI). The data is computational code or logic programming.
For example, using AI in cameras that detect faces, facial shapes or AI that detects facial recognition in animals, for example AI cameras that can recognize faces of cats and dogs.
While Machine Learning is in the environment of data representation or science in mathematical form. This method is like a tool, how to perform various tasks to solve a problem with an approach in artificial intelligence (AI).
This method studies various points in the development of machine systems using existing data, these data are based on past human habits and experiences.
2. Use of Machine Learning in various industries
We have felt the various benefits and goals of Machine Learning at this time, as well as in industry or other fields. Some examples of implementation in various industries:
1. Health
Machine Learning helps in various methods in the world of health to quickly analyze and identify a disease such as cancer or heart disease.
This method can identify quickly compared to manually, of course if manually it will require a lot of energy to process data or the monitoring process.
2. Banking
Usually the use of Machine Learning in banking can identify customers who meet the requirements to be given a number of credit cards or loans so that later they are not likely to fail in making payments to the bank.
3. Device
Technology that is growing rapidly in the smartphone industry has been able to extract various information using the Machine Learning method. Unlike in the past, mobile phones can only be used for certain purposes such as making calls or SMS.
Currently, many flagship smartphones such as the iPhone or Samsung have implemented this method in their smartphones, such as facial recognition and facial scans on humans.
4. Information
Machine Learning is very commonly used by big companies like Google or Microsoft. As an example of its use, namely in managing information that we usually encounter in everyday life such as language translators on the Google Translate service.
Various information that we input will be processed by the machine to provide the output we want. This is certainly very helpful in our daily life.
3. Then how does Machine Learning work?
Research and algorithms determine how Machine Learning works. This pattern can make various predictions involving various structural processes to build better machines. Some of the stages of how Machine Learning works are:
1. Collecting data
Some data is collected in various forms, it can be with Excel or others. This step aims to validate data, train data or machine learning to be relevant.
2. Data analysis
Data analysis is needed to determine the quality of the data to be used by the machine. These data need to be evaluated and corrected if there are problems.
3. Data modeling
Building data modeling by involving appropriate algorithms and representations aims to achieve features that are in accordance with Machine Learning.
Data model testing can be done when data validation is good to get the appropriate input, process and output.
4. Model test and optimization
Doing test models to test the accuracy of the data used. The test data compares the validated performance and then applies the trained data to create new data predictions.
This step will determine what algorithm will be used, to achieve certain goals.
4. Types of Machine Learning Algorithms
There are 3 types of Machine Learning algorithms that you need to know including Supervised Learning, Unsupervised Learning and Reinforcement Learning. Here's the explanation:
1. Supervised Learning
Supervised Learning Algorithm is used to predict based on historical data in the past. Supervised Learning has been given clear instructions for what stages to learn.
K-Nearest Neighbor (KNN) and Naive Bayes are some examples of the algorithms used. For example in knowing natural conditions such as earthquakes, tornadoes and others.
2. Unsupervised Learning
Unsupervised Learning Algorithm is used to study data by taking a data approach which is commonly called Clustering or drawing conclusions from datasets. For example in the health industry, to predict what diseases can occur simultaneously with heart disease.
K-Means Clustering Algorithm is an example of the algorithm used.
3. Reinforcement Learning
Reinforcement Learning Algorithm is different from Supervised Learning and Unsupervised Learning. This algorithm is used to make the computer can learn itself from the environment or the environment through an agent. Which means the computer performs self discovery and interacts with the environment directly.
Machines are trained continuously to make specific decisions based on business needs to maximize efficiency.
This process is very time-saving because it does not involve many people. Markov Decision Process is an example of the algorithm used.
Closing
By knowing what Machine Learning or Artificial Intelligence is, you can easily broaden your horizons in the digital world, Machine Learning or Artificial Intelligence is now a promising business opportunity in the digital world. Because now many companies are using Machine Learning and Artificial Intelligence in their business.
