Machine learning is a means of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that machines can learn from data, identify patterns and make decisions without any pre-programmed rules. In this article, we are going to cover some points:
But let’s go more in-depth on the definition..
What is Machine Learning?
“Machine learning is a computer program said to learn from experience ‘E’ with respect to some class of tasks ‘T’ and performance measure ‘P’, if its performance at tasks in ‘T’, as measured by ‘P’, improves with experience ‘E’.” — Tom Mitchell
The machine learning field is a quite vast and is expanding quickly. It continually partitions into subcategories and different types of machine learning. ML is an important aspect of modern business and research. By using algorithms and neural network models, it can assist computer programs in progressively improving their performance with minimal human intervention.
The same way that when humans are born being initially incapable of performing any useful function or task until taught over time, computers can learn in the same manner. As a simplified example, if you want to create a computer program that can learn to identify whether an animal is a dog or cat, you would feed it many images of dogs and cats. Eventually the computer system would be able to use statistical models based on previous data to identify whether it is looking at a cat or dog.
Machine learning algorithms are often categorised into several types. This article will cover four of the most common types of machine learning:
- Supervised ML
- Unsupervised ML
- Semi-supervised ML
- Reinforcement ML
But before we delve into those areas, lets first explore a brief history.
History of Machine Learning
Arthur Samuel, an American pioneer in the field of artificial intelligence and computer gaming, coined the term machine learning in 1959. While working at IBM, Samuel published a study in that year where he expressed a digital computer’s capability of behaving in such a way, “which, if done by human beings or animals, would be described as involving the process of learning”. He expressed the notion that, at the time, computers existed with adequate data-handling abilities to make use of machine-learning techniques, but were limited by mankind’s knowledge of these techniques.
Samuel developed a program that played checkers on a championship level using his basic checker-playing program. Using a tree-based decision making model, his program learned to play while recognising most winning and losing end positions many moves in advance.
Since the computer program had a very small amount of computer memory available, Samuel created what is called “alpha-beta pruning”. Alpha-beta pruning is an adversarial search algorithm used commonly for machine playing of two-player games such as chess, checkers, etc. It essentially stops evaluating a move when at least one possibility has been found that proves the move to be worse than a previously examined move.