Abstract

Since the beginning of the Industrial Revolution, machines have made great strides. They continue to be a common sight on factory floors and in manufacturing plants, but their capabilities have evolved to the point where they can now perform cognitive tasks in addition to the manual labour that was previously the exclusive domain of humans. There are many specific complex tasks that machines are now capable of simulating, such as judging music competitions, driving automobiles, and playing chess with professional players. Other examples include mopping the floor with professional chess players. However, studies on the planned automation of jobs and predictions about the future development of machines and artificial intelligence (AI) should be read with a healthy dose of skepticism. The development of AI technology is accelerating, but widespread implementation is still in its infancy and faces a number of known and unknown obstacles. There will inevitably be snags, holdups, and other hurdles. The concept of machine learning is also not as straightforward as turning a switch and then asking the computer to make you a tasty martini while predicting the winner of the Super Bowl. In the realm of problem-solving, machine learning is not even close to being a “out-of-the-box” option. Skilled personnel, also known as data scientists and machine learning engineers, are responsible for managing and supervising the statistical algorithms that are the basis for the operation of machines. In this particular labour market, the number of available jobs is expected to increase in the future, but the supply is now having trouble keeping up with demand. The lack of an adequate supply of professionals who possess the necessary expertise and training is one of the most significant obstacles that is delaying the progress of artificial intelligence, according to industry experts who lament this fact. This book focuses on the high-level basics of machine learning as well as the mathematical and statistical underpinnings of creating machine learning models. The book is written as per the common University syllabus compiled from Indian Universities to facilitate the B.E., B.Tech., MSc., and MCA students.

Keywords:

Machine Learning, Artificial Intelligence

References

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How to cite this Book: 

APA:
Rameshbabu, V. Vijayakumaran, C. Edwin Prabhakar, P.B. (2022). Machine Learning (1st ed., pp. 1-212). Jupiter Publications consortium,ISBN:978-93-91303-37-2, DOI: https://doi.org/10.47715/JPC.B.82.2022.9789391303372