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物品 #:
74381285
An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)
86% of respondents would recommend this to a friend
物品 #:
74381285
HKD 589
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What Stands Out
Practical Applications
Focuses on real-world applications of statistical learning using Python, making it accessible for practitioners and students alike.
Comprehensive Coverage
Covers a vast range of statistical methods and machine learning techniques, providing a thorough foundation for learners at all levels.
User-Friendly Approach
Designed with clarity in mind, featuring detailed explanations and examples that simplify complex concepts for easier comprehension.
產品詳情
- An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
| Publisher | Springer |
| Publication date | July 1, 2023 |
| Edition | 2023rd |
| Language | English |
| Print length | 622 pages |
| ISBN-10 | 3031387465 |
| ISBN-13 | 978-3031387463 |
| Item Weight | 3.6 pounds (1.63 kg) |
| Dimensions | 7.17 x 1.65 x 10.08 inches (18.2 x 4.2 x 25.6 cm) |
| Part of series | Springer Texts in Statistics |
Who Should Buy?
-
Beginner Students
Ideal for students new to statistical learning and looking for clear explanations and practical applications using Python.
-
Data Scientists
Useful for data scientists seeking to enhance their statistical analysis skills with hands-on Python projects and exercises.
-
Educators
Great resource for instructors who want a comprehensive textbook for teaching statistical learning methodologies in Python.
-
Advanced Statisticians
May not meet the needs of advanced statisticians looking for in-depth theoretical insights beyond practical applications.
產品敘述
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Probability & Statistics Editorial Review
An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics) offers a robust framework for understanding machine learning fundamentals. Published by Springer in July 2023, this edition introduces essential updates such as new chapters on Neural Networks, making it relevant for current practices. As noted in reviews, the book effectively explains concepts with clear examples and visualizations, providing a self-contained resource ideal for graduate courses or personal enrichment. However, users have raised concerns about the binding of the paperback edition, indicating that pages may come loose with heavy use. Overall, it serves as an excellent reference for those looking to strengthen their grasp on statistics within machine learning contexts.
Customer Reviews & Ratings
160 客戶評分
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86%
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3 星
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優點
- New chapters reflect current trends in machine learning.
- Clear explanations and well-structured examples.
- Visualizations are easy to read and understand.
- Self-contained resource with comprehensive coverage.
- Highly recommended for graduate-level courses.
缺點
- Binding of the paperback edition can be problematic.
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HKD 589
現在訂購，約可在下列時間收貨： 星期五, 十二月 11
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