Machine learning algorithms and applications The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. Jul 13, 2016 · This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical Jan 6, 2022 · The chapters in the book illustrate how machine learning and deep learning algorithms and models are designed, optimized, and deployed. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. These algorithms form the core of intelligent systems, empowering organizations to analyze patterns, predict outcomes, and automate decision-making processes. Apr 21, 2025 · Machine Learning (ML) Algorithms are the backbone of everything from Netflix recommendations to fraud detection in financial institutions. Aug 19, 2016 · This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied. . It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. rjvmirghrvvgmpygvmcfumosrhfkexvuvbrzgrjdueebyfp