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This book presents machine learning as a set of pre-requisites,
co-requisites, and post-requisites, focusing on mathematical
concepts and engineering applications in advanced welding and
cutting processes. It describes a number of advanced welding and
cutting processes and then assesses the parametrical
interdependencies of two entities, namely the data analysis and
data visualization techniques, which form the core of machine
learning. Subsequently, it discusses supervised learning,
highlighting Python libraries such as NumPy, Pandas and Scikit
Learn programming. It also includes case studies that employ
machine learning for manufacturing processes in the engineering
domain. The book not only provides beginners with an introduction
to machine learning for applied sciences, enabling them to address
global competitiveness and work on real-time technical challenges,
it is also a valuable resource for scholars with domain knowledge.
This book presents machine learning as a set of pre-requisites,
co-requisites, and post-requisites, focusing on mathematical
concepts and engineering applications in advanced welding and
cutting processes. It describes a number of advanced welding and
cutting processes and then assesses the parametrical
interdependencies of two entities, namely the data analysis and
data visualization techniques, which form the core of machine
learning. Subsequently, it discusses supervised learning,
highlighting Python libraries such as NumPy, Pandas and Scikit
Learn programming. It also includes case studies that employ
machine learning for manufacturing processes in the engineering
domain. The book not only provides beginners with an introduction
to machine learning for applied sciences, enabling them to address
global competitiveness and work on real-time technical challenges,
it is also a valuable resource for scholars with domain knowledge.
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