Machine and Deep Learning Algorithms and Applications

This book provides a comprehensive overview of machine learning paradigms, including supervised, unsupervised, and semi-supervised learning, along with deep learning techniques.

In Machine and Deep Learning Algorithms and Applications, readers are introduced to essential concepts and applications of machine learning, making it accessible to a wide audience, including students, faculty, and industry professionals. The book begins with an overview of how machine learning empowers computers and embedded systems to learn from data, emphasizing the importance of training data in improving model performance. It categorizes machine learning paradigms into three main types: supervised learning, unsupervised learning, and semi-supervised learning, each with its unique characteristics and applications.

Supervised learning algorithms are designed to learn a mapping function from labeled data, further divided into classification and regression tasks. In contrast, unsupervised learning focuses on uncovering hidden patterns within unlabeled data. The book also explores semi-supervised learning, which effectively combines large volumes of unlabeled data with a limited amount of labeled data to enhance model performance. A significant portion is dedicated to deep learning, a subfield of machine learning that leverages artificial neural networks and has gained traction due to advancements in computational power and data availability.

The structure of Machine and Deep Learning Algorithms and Applications is thoughtfully organized, starting with foundational concepts before delving into specific algorithms and their workings. The book also highlights various applications of machine learning across different sectors, including industry, government, and healthcare, and concludes with a summary of key concepts and an extensive bibliography for further reading.