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91353563
An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)
88% of respondents would recommend this to a friend
товар №:
91353563
TJS 1378
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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.
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What Stands Out
Comprehensive Coverage
Offers an in-depth exploration of statistical learning techniques, bridging theory and practical application in Python, making it ideal for both students and practitioners in data science.
Hands-On Applications
Includes real-world examples and coding exercises, enabling learners to apply statistical concepts directly using Python, enhancing understanding and engagement with essential tools.
Updated Content
The 2023 edition features the latest advancements in statistical learning, ensuring readers are equipped with the most current methodologies and practices to stay competitive in the field.
Информация о продукте
- 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 2, 2024 |
| Edition | 2023rd |
| Language | English |
| Print length | 622 pages |
| ISBN-10 | 3031391896 |
| ISBN-13 | 978-3031391897 |
| Item Weight | 7.4 ounces (209.79 grams) |
| Dimensions | 6.4 x 1.1 x 9.7 inches (16.3 x 2.8 x 24.6 cm) |
| Part of series | Springer Texts in Statistics |
Who Should Buy?
-
Data Science Students
Ideal for students studying data science, as it covers foundational statistical concepts with practical applications in Python.
-
Beginner Statisticians
Great for beginners who want to understand statistical learning concepts without advanced mathematical prerequisites.
-
Professionals in Analytics
Useful for professionals in analytics looking to enhance their skills in statistical modeling and data analysis using Python.
-
Advanced Statisticians
Not suitable for advanced statisticians seeking in-depth theoretical discussions or complex statistical methodologies.
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Probability & Statistics Editorial Review
**** "An Introduction to Statistical Learning: with Applications in Python" (2023rd Edition) has garnered a broadly favorable reception among its users, highlighting its robust application to both academic settings and self-study endeavors. Reviewers commend the book's content, particularly its updated chapters that reflect contemporary practices in the field, such as neural networks and deep learning. The inclusion of Python as a programming language marks a pivotal enhancement, aligning with current industry standards. Many users have noted the clarity of the printed material and Consider the book an essential resource for learning statistical methods and machine learning. However, while the content receives high praise for its depth and clarity, several reviewers have raised concerns about the physical quality of the paperback and hardcover editions, specifically the bookbinding. Many found the binding inadequate, leading to pages coming loose within a few months of use, which detracts from the overall reading experience. Despite these concerns, the content's strength and relevance to learners seem to outweigh the physical shortcomings for most users. While some users noted minor issues with outdated Python code explanations, others found the book to be a comprehensive guide and essential resource for anyone in the field of statistics or data science. Overall, the reception of this edition is overwhelmingly positive, with high recommendations for its educational value. **
Customer Reviews & Ratings
159 оценки клиентов
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5 звезда
86%
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4 звезда
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3 звезда
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Плюсы
- Comprehensive and well-structured content reflecting modern practices.
- Inclusion of updated chapters on topics like neural networks and deep learning.
- Clarity of printing and informative Python applications.
- Highly recommended for academic courses and self-study.
Минусы
- Poor binding quality, leading to pages detaching over time in paperback editions.
Product Price History
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Особенности и преимущества
- Comprehensive guide to statistical learning techniques.
- Covers essential tools for data analysis across various fields.
- Includes topics like regression, classification, and deep learning.
- Real-world examples and color graphics enhance understanding.
- Designed for both statisticians and non-statisticians.
- Python-based labs included for practical application.
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