Giovanni Cerulli
Fundamentals of Supervised Machine Learning: With Applications in Python, R, and Stata
Fundamentals of Supervised Machine Learning: With Applications in Python, R, and Stata
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- More about Fundamentals of Supervised Machine Learning: With Applications in Python, R, and Stata
This book provides a comprehensive introduction to supervised machine learning with Python, R, and Stata, covering model selection, regularization, and applications in various fields. It is suitable for PhD students, researchers, and practitioners with a background in statistics and software.
Format: Hardback
Length: 391 pages
Publication date: 27 November 2023
Publisher: Springer International Publishing AG
This comprehensive book delves into the intricate realm of supervised machine learning, offering a comprehensive theoretical foundation alongside a diverse range of practical applications utilizing Python, R, and Stata. It strikes a harmonious balance between theory and practice, fostering a deep understanding and awareness of the diverse range of machine learning methods available across various software platforms.
After laying the theoretical groundwork, the book proceeds to explore a wide array of topics, including model selection and regularization, discriminant analysis, nearest neighbors, support vector machines, tree modeling, artificial neural networks, deep learning, and sentiment analysis. Each chapter stands independently, featuring an initial theoretical section that elucidates the core principles of the methodologies, followed by an applied section where the methods are applied to real-world datasets. Numerous illustrative examples are provided, accompanied by comprehensive Python, R, and Stata codes, along with the corresponding datasets, to facilitate ease of reproducibility.
Designed with a primary target audience of Ph.D. students, researchers, and practitioners from diverse fields such as economics, social sciences, medicine, and epidemiology, this book assumes a solid grasp of basic statistics and a working knowledge of statistical software. It caters to those seeking to harness the power of machine learning techniques in their research and professional endeavors.
By comprehensively covering the theoretical foundations and practical applications of supervised machine learning, this book serves as a valuable resource for scholars and practitioners alike, empowering them to unlock the full potential of this powerful technology in their respective domains.
Weight: 836g
Dimension: 241 x 161 x 29 (mm)
ISBN-13: 9783031413360
Edition number: 1st ed. 2023
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