Data Science And Machine Learning Mathematical And Statistical Methods(1st Edition)
Authors:
Dirk P. Kroese, Thomas Taimre, Radislav Vaisman, Zdravko Botev
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Solution Manual Includes
Price:
$9.99
Book details
ISBN: 1138492531, 978-1138492530
Book publisher: Chapman And Hall/CRC
Book Price $0 : 'Data Science and Machine Learning: Mathematical and Statistical Methods' by Dirk P. Kroese and colleagues is a comprehensive guide that delves into the mathematical and statistical foundations underpinning modern data science and machine learning. This book is structured to provide a cohesive understanding of fundamental concepts such as probability theory, statistical inference, and stochastic processes, which are crucial for anyone seeking to build algorithms and data models. The 'table of content' is meticulously organized, enabling readers to navigate through complex topics like supervised and unsupervised learning, regression analysis, and Bayesian methods with ease. Throughout its chapters, the text illustrates key methodologies like Monte Carlo simulation, optimization techniques, and linear models, emphasizing their applications in real-world data-driven environments. Significant attention is paid to computational aspects, with frequent references to programming languages like Python and R, which are essential tools for practitioners to implement algorithms effectively. This book also serves as an invaluable resource for solving practical exercises, as it often refers to a 'solution manual' and 'answer key' that help reinforce understanding and application of intricate machine learning theories. The book has been well-received in academic circles for its depth and clarity, making it an indispensable resource for both novice and seasoned professionals in the field of data science. It provides a structured pathway for mastering the mathematical skills needed to excel in creating robust, scalable machine learning solutions. With an increased focus on statistics and computational solutions, data science enthusiasts can enhance their algorithmic proficiency and analytical capabilities. Offering cheap access to complex topics, this book helps students excel affordably.
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