Elements of Data Science, Machine Learning, and Artificial Intelligence Using R
Oct 2023 · Springer Nature
4.0star
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Ebook
575
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About this ebook
The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.
Ratings and reviews
4.0
2 reviews
Ottilie Thomas
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October 15, 2024
This book is a goldmine for anyone looking to dive into the world of data science, machine learning, and artificial intelligence using R. The authors do a fantastic job of breaking down complex concepts into understandable terms, making it accessible to both beginners and experienced programmers. The book covers a wide range of topics, from data cleaning and preprocessing to advanced machine learning algorithms and deep learning techniques. With plenty of practical examples and exercises
Sarah Pfeiffer
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October 15, 2024
This book is a goldmine for anyone looking to dive into the world of data science, machine learning, and artificial intelligence using R. The authors do a fantastic job of breaking down complex concepts into understandable terms, making it accessible to both beginners and experienced programmers. The book covers a wide range of topics, from data cleaning and preprocessing to advanced machine learning algorithms and deep learning techniques. With plenty of practical examples and exercises
About the author
Frank Emmert-Streib is Professor of Data Science at Tampere University (Finland). He leads the Predictive Society and Data Analytics Lab, which pursues innovative research in deep learning and natural language processing. The Lab develops and applies high-dimensional methods in machine learning, statistics and artificial intelligence that can be used for knowledge extraction of data from biology, medicine, social media, social sciences, marketing or business.
Salissou Moutari is Senior Lecturer at Queen’s University Belfast (UK) and Interim Director of Research of the Mathematical Science Research Centre (MSRC). His research interests include mathematical modelling, optimization, machine learning and data science, and the applications of these methods to problems from traffic, transportation and distribution systems, production planning and industrial processes.
Salissou Moutari is Senior Lecturer at Queen’s University Belfast (UK) and Interim Director of Research of the Mathematical Science Research Centre (MSRC). His research interests include mathematical modelling, optimization, machine learning and data science, and the applications of these methods to problems from traffic, transportation and distribution systems, production planning and industrial processes.
Matthias Dehmer is Professor at UMIT (Austria) and also has a position at Swiss Distance University of Applied Sciences, Brig, Switzerland. His research interests are in complex networks, complexity, data science, machine learning, big data analytics, and information theory. In particular, he is working on machine learning based methods to analyse high-dimensional data.
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