Machine Learning with R / by Abhijit Ghatak
Material type: TextPublication details: New York : Springer c 2017Description: xix, 210 p: ill; 24 cmISBN: 9789811068072 (hbk)Subject(s): Machine LearningGenre/Form: Electronic books.DDC classification: 006.31 Summary: Annotation This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it's applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning.In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation.The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning.Item type | Current library | Call number | Status | Date due | Barcode | Item holds |
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Books | Namal Library Computer Science | 006.31 GHA-M 2017 10282 (Browse shelf (Opens below)) | Available | 0010282 |
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006.31 BUR-H 2019 12211 The hundred-page machine learning book / | 006.31 BUR-M 2020 12212 Machine learning engineering / | 006.31 GER-H 2019 12209 Hands-on machine learning with Scikit-Learn and TensorFlow : concepts, tools, and techniques to build intelligent systems / | 006.31 GHA-M 2017 10282 Machine Learning with R / | 006.31 HSI-M 2010 3565 Machine learning methods in the environmental sciences : | 006.31 MUE-M 2021 12208 Machine learning for dummies / | 006.31 PAU-M 2018 10248 Machine learning (in python and R) dummies / |
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Annotation This book helps readers understand the mathematics of machine learning, and apply them in different situations. It is divided into two basic parts, the first of which introduces readers to the theory of linear algebra, probability, and data distributions and it's applications to machine learning. It also includes a detailed introduction to the concepts and constraints of machine learning and what is involved in designing a learning algorithm. This part helps readers understand the mathematical and statistical aspects of machine learning.In turn, the second part discusses the algorithms used in supervised and unsupervised learning. It works out each learning algorithm mathematically and encodes it in R to produce customized learning applications. In the process, it touches upon the specifics of each algorithm and the science behind its formulation.The book includes a wealth of worked-out examples along with R codes. It explains the code for each algorithm, and readers can modify the code to suit their own needs. The book will be of interest to all researchers who intend to use R for machine learning, and those who are interested in the practical aspects of implementing learning algorithms for data analysis. Further, it will be particularly useful and informative for anyone who has struggled to relate the concepts of mathematics and statistics to machine learning.
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