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Item #:
74381285
An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)
86% of respondents would recommend this to a friend
Item #:
74381285
JMD 16774
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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.
Product Details
| 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.
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Educators
Great resource for instructors who want a comprehensive textbook for teaching statistical learning methodologies in Python.
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Advanced Statisticians
May not meet the needs of advanced statisticians looking for in-depth theoretical insights beyond practical applications.
Product Description
An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)
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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.
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Pros
- 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.
Cons
- Binding of the paperback edition can be problematic.
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