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Machine Learning: General Concept
Since pre-historic era of Industrial revolution, machines have come a long way but now tend to extensively extend their capabilities and activities to performing cognitive tasks which were thought to be done only by humans like driving automobiles, object detection, pattern recognition and many more. Since then, automation in industrial machineries and professional jobs has always been a hot topic. According to the British Broadcasting Company (BBC), professions such as bar worker (77 %), waiter (90 %), chartered accountant (95 %), receptionist (96 %) and taxi driver (57 %) are on the verge of becoming fully automated by 2035. Although Artificial Intelligence (AI) and Machine Learning (ML) technologies are moving fast, adoption of them can be significantly delayed due to the unforeseen challenges and other obstacles which are inevitable.
Charles Green, the Director of Thought Leadership at Belatrix Software states:
“It’s a huge challenge to find data scientists, people with machine learning experience, or people with the skills to analyse and use the data, as well as those who can create the algorithms required for machine learning. Secondly, while the technology is still emerging, there are many ongoing developments. It’s clear that AI is a long way from how we might imagine it.”
This statement, in one way, indicates a path and scope towards becoming an expert in the field of Machine Learning. You must first understand basic classical statistics to build and program intelligent machines while computer programming is another indispensable part of Machine Learning you can’t avoid.
In 1959, IBM’s Arthur Samuel had published a paper in the IBM Journal of Research and Development that focused on the use of Machine Learning in the game of checkers “to verify the fact that a computer can be programmed so that it will learn to play a better game of checkers than can be played by the person who wrote the program.” The paper “Some studies in Machine Learning using the Game of Checkers” by Arthur Samuel was not the first to use the term “Machine Learning” but he is widely considered as the first person to coin and define Machine Learning topic, which we know as of today.
Machine Learning: Basic Introduction
Thus, Machine Learning can be defined as the systems with more intelligence and with the ability to learn automatically and improve continuously from experiences rather than being programmed explicitly. The most important feature of Machine Learning is the “Self-learning” ability of the system. This “self-learning” behavior in Machine Learning is achieved through the application of statistical modelling to detect pattern and improve the performance for themselves without explicit programming. Machine Learning can adapt and modify its behaviour by reacting to errors while traditional programming can be highly susceptible to such errors.
Machine Learning has mainly three steps: Data -> Model -> Action.
The data in Machine Learning is divided into two types: training data and test data. The training data is used to develop the model and provide training to that model while the remaining data known as test data is then used to test the model once the model has been successfully developed based on training data and has proven satisfactory accuracy. The model is a statistical-based rule which is then trained and tested with the data.
Machine Learning Categories:
There are many more statistical-based algorithms for Machine Learning and choosing the right algorithm (or combination of algorithms) for your particular job is always challenging for anyone working in this field. Before diving deep into the specific algorithm, let’s understand the basic three categories of Machine Learning which are Supervised Learning, Unsupervised Learning and Reinforcement. Click on the link below to know in detail.