Healthbeautyidea.com – In recent years, many have started to learn machine learning. This cannot be separated from the development of computing technology and data storage that is increasingly cheap. But not everyone understands what machine learning is.
In this article, we will focus on one of the branches of artificial intelligence, namely machine learning (ML). ML is a technology that is able to learn existing data and perform certain tasks according to what it learns. Before we discuss more about machine learning, let’s explore the definition.
What is machine learning?
By definition, machine learning is the science or study of algorithms and statistical models used by computer systems to perform specific tasks without explicit instruction. Machine learning depends on patterns and conclusions. To get these patterns and conclusions, machine learning algorithms produce mathematical models based on sample data that is often called ‘training data.’
The development of machine learning-handled data includes key insights from artificial intelligence and decision-making in applications or businesses. The existence of machine learning is said to be able to influence the ideal growth matrix of databases in the world of information technology business.
As data grows and grows, the market demand for data scientists will also increase. This will require data experts to identify the most relevant business questions and then synchronize the data to answer them. The role of machine learning exists in the adjustment between these ever-evolving data-related questions and answers.
History of machine learning
The term machine learning was coined in 1959 by Arthur Samuel, an American IBM and a pioneer in the fields of computer games and artificial intelligence. A representative book of machine learning research during the 1960s was Nilsson’s book on Learning Machines, which mostly addressed machine learning for pattern classification.
This interest in machine learning pattern recognition continued into the 1970s, as described by Duda and Hart’s research in 1973. The next time in 1981, a report was given on the use of teaching strategies so that neural networks learned to recognize 40 characters (26 letters, 10 numbers, and 4 special symbols) from computer terminals.
Why machine learning is so important?
Along with the rise of revolutionary industrial applications of automation systems that lack human error, machine learning involves computers discovering how they can perform tasks without being explicitly programmed. For simple tasks assigned to a computer, it is possible to program algorithms that tell the machine how to execute all the steps necessary to solve the problem at large; On the computer, no learning is required.
For further tasks, it can be challenging for humans to manually create the required algorithms. In practice, it could be more effective to help machines develop their own algorithms, rather than having human programmers determine every step needed.
The discipline of machine learning uses a variety of approaches to help computers learn to complete tasks where no algorithm is fully satisfactory. In cases where there are a large number of potential answers, one approach is to label some correct answers as valid. It can then be used as training data for computers to improve the algorithms used to determine the correct answer. For example, to train a system for digital character recognition tasks, MNIST datasets are often used.
What is the difference between Machine Learning and Artificial Intelligence (AI)?
AI refers to the procedure of programming a computer (machine) to take something rational. What is rational? Rational is the basis for making decisions.
For example, AI is used to check whether certain parameters in a program behave normally. For example, a machine can raise an alarm if the parameter says ‘X’ crosses a certain threshold which in turn can affect the outcome of the related processes.
Application of AI in machine learning
Machine learning is a subset of AI where machines are trained to learn from past experiences. Past experience is developed through collected data, then combines with algorithms (such as Naïve Bayes, Support Vector Machine (SVM)) to give the final result.
What is the difference between Machine Learning and Statistics?
Statistics is a branch of mathematics that utilizes data from either an entire population or sample to perform analysis and present conclusions. Some of the statistical techniques used are regression, variance, standard deviation, conditional probability and others.