10/10/2023 0 Comments Basic algorithm of life concept map![]() ![]() The data can be structured, semi-structured, or unstructured, discussed briefly in Sect. For instance, the current electronic world has a wealth of various kinds of data, such as the Internet of Things (IoT) data, cybersecurity data, smart city data, business data, smartphone data, social media data, health data, COVID-19 data, and many more. ![]() We live in the age of data, where everything around us is connected to a data source, and everything in our lives is digitally recorded. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view. We also highlight the challenges and potential research directions based on our study. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. 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. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. The book is intended for a broad audience acquainting the reader with the theoretical sides of computer programming, for students studying computer related fields.In the current age of the Fourth Industrial Revolution (4 IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. Readership: Advanced undergraduate 2nd year of Computer Science, engineering, data science. Press Release - Basic algorithms for undergraduates in a concise and readable form.Each chapter comes with its own set of exercises, and solutions to most of them are appended. The topics include Divide and Conquer, Dynamic Programming, Graph algorithms, probabilistic algorithms, data compression, numerical algorithms and intractability. Most of the classical and some more advanced subjects in the theory of algorithms are covered, though not in a comprehensive manner. Klein reproduces his oral teaching style in writing, with one topic leading to another, related one. It is primarily intended as a textbook for the teaching of Algorithms for second year undergraduate students in study fields related to computers and programming. Its coverage includes the algorithms' design process and an analysis of their performance. Basic Concepts in Algorithms focuses on more advanced paradigms and methods combining basic programming constructs as building blocks and their usefulness in the derivation of algorithms. It is self-contained but does assume some prior knowledge of data structures, and a grasp of basic programming and mathematics tools. This book is the result of several decades of teaching experience in data structures and algorithms. ![]()
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