An Introduction to Statistical Learning
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An Introduction to Statistical Learning
With Applications in Python
$32.90
$78.90
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An Introduction to Statistical Learning introduces readers to essential statistical learning and machine learning concepts used in modern data analysis. The book covers supervised and unsupervised learning techniques, including regression, classification, resampling, tree methods, support vector machines, clustering, and neural networks. Each topic is explained using clear language, visual examples, and practical applications to help readers build strong foundational knowledge.
The book is especially valued for its approachable teaching style and practical focus. It includes exercises, real-world datasets, and R programming examples that allow readers to implement statistical models directly. This makes it highly suitable for undergraduate and graduate students, data science beginners, business analysts, and professionals seeking practical machine learning knowledge. The book provides a strong balance between theory and application, making advanced statistical concepts more understandable and useful in real-world scenarios.
Gareth James is a respected statistician, educator, and academic known for his contributions to statistical learning, data science, and predictive analytics. He has taught and researched extensively in the fields of machine learning and applied statistics. Along with co-authors Daniela Witten, Trevor Hastie, and Robert Tibshirani, he created one of the world’s most popular introductory textbooks on statistical learning, widely used in universities and professional data science programs.
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