- Published Year: 2017
- Page count: 440
- File Size: 8 MB
- Language: English
- Published by: iBooker it-ebooks
- Visited by: 154
- Rating/Review: 4.6
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ISBN:
9781461471370
9781461471387
2013936251
کانال تلگرام امین مصباحی
تِکافترنون، رویدادی گاهبهگاه است با موضوعات حول معماری و توسعه نرمافزار، این کانال هم برای اشتراک اخبار، آموزش، نکاتی حول مهندسی نرمافزار، دیتابیس، تکنولوژی و مدیریت تولید محصولات نرمافزاری خواهد بود. امین مصباحی
Learn MoreAn Introduction to Statistical Learning
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Persian Summary
Introduction to "An Introduction to Statistical Learning"
Welcome to "An Introduction to Statistical Learning," a comprehensive guide that delves into the core concepts and techniques of statistical learning and data analysis. This book serves as a valuable resource for data scientists, statisticians, and anyone interested in understanding how to extract meaningful insights from data.
Detailed Summary
Statistical learning is the science of learning from data. With the exponential growth in data and computing power, the ability to extract actionable insights from complex datasets is critical. "An Introduction to Statistical Learning" provides a systematic approach to understanding and implementing statistical models in practical scenarios.
The book covers a broad range of topics essential for statistical learning. Starting with a foundation in simple linear regression, it progresses through more complex methodologies such as multiple regression, classification, resampling methods, and tree-based methods. It also delves into unsupervised learning and boosting techniques.
Each chapter is carefully structured to include theoretical understanding, practical applications, and coding examples, making it particularly accessible to those new to the field. The authors leverage their expertise to explain intricate concepts, ensuring that readers can translate theory into practice effectively.
Key Takeaways
- Comprehensive coverage of statistical learning techniques, from basic to advanced.
- Emphasis on both theoretical understanding and practical implementation.
- Inclusion of R code snippets to demonstrate the application of discussed concepts.
- Clear explanations aimed at readers with varied levels of statistical and programming background.
- Focus on real-world applications, preparing readers for the complexities of modern data challenges.
Famous Quotes
"Statistical learning deals with the problem of finding a predictive function based on data."
"Good data science is about engineering features, using your domain knowledge, and creating statistical models that make sense."
"To predict what consumers want, one must first understand how they think."
Why This Book Matters
In a world where data is often considered the 'new oil', the ability to effectively analyze and make predictions from data is a highly sought skill. This book equips readers with the necessary tools to tackle real-world statistical problems proficiently.
The significance of "An Introduction to Statistical Learning" lies in its ability to distill complex statistical concepts into understandable terms that are useful for practical applications. It offers a blend of theory, intuition, and hands-on experimentation, ensuring that readers not only understand the algorithms but also know how to apply them to solve practical problems.
Furthermore, the book provides an excellent precursor to more advanced literature in statistical learning, making it a stepping stone for learners aiming to delve deeper into machine learning and data science.
Its accessibility to individuals with varying expertise, coupled with the real-world case studies and examples, makes it invaluable not just to students and academics, but also to professionals working across various data-intensive industries.
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