Название: Machine Learning Methods for Engineering Application Development
Автор: Prasad Lokulwar, Basant Verma, N. Thillaiarasu
Издательство: Bentham Books
Год: 2022
Страниц: 240
Язык: английский
Формат: pdf (true), epub
Размер: 26.1 MB

This book is a quick review of Machine Learning methods for engineering applications. It provides an introduction to the principles of Machine Learning and common algorithms in the first section. Proceeding chapters summarize andanalyze the existing scholarly work and discuss some general issues in this field. Next, it offers some guidelines on applying Machine Learning methods to software engineering tasks. Finally, it gives an outlook into some of the futuredevelopments and possibly new research areas of Machine Learning and Artificial Intelligence in general. Techniques highlighted in the book include: Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural network. Finally, it also intends to be a reference book.

Key Features: Describes real-world problems that can be solved using machine learning Explains methods for directly applying machine learning techniques to concrete real-world problems Explains concepts used in Industry 4.0 platforms, including the use and integration of AI, ML, Big Data, NLP, and the Internet of Things (IoT). It does not require prior knowledge of the Machine Learning

Computers, systems, applications, and technology, in general, are becoming more commonly used, advanced, scalable, and thus effective in modern times. Because of its widespread use, it undergoes various advancements on a regular basis. A fast-paced life is also associated with modern times. This way of life necessitates that our systems behave similarly. Adaptive Machine Learning (AML) can do things that conventional machine learning cannot. It will easily adjust to new information and determine the significance of that information. Adaptive machine learning uses a variety of data collection, grouping, and analysis methods due to its single-channeled structure. It gathers, analyses, and learns from the information. That is why it is adaptive: as long as new data is presented, the system can learn and update. This single-channeled device acts on any piece of input it receives in order to improve potential forecasts and outcomes. Furthermore, since the entire process happens in real-time, it can immediately adjust to new actions. High efficiency and impeccably precise accuracy are two of AML's main advantages. The system does not become outdated or redundant because it is constantly running in real-time. So, incorporating the three core concepts of agility, strength, and efficiency better explains AML.

This book is meant to be an introduction to Artificial Intelligence (AI), Machine Learning, and its applications in Industry 4.0. It explains the basic mathematical principles but is intended to be understandable for readers who do not have a backgroundin advanced mathematics.