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An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)

Name: An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)
Price: 88.26 USD
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3.9 of 5 Stars: Programming - An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)

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Book Synopsis: 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.

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If you're looking to dive into the world of statistical analysis and machine learning, look no further than "An Introduction to Statistical Learning: with Applications in Python." This comprehensive book, part of the Springer Texts in Statistics series, is a must-have for both beginners and experienced data scientists. With its practical approach and emphasis on real-world applications, this book teaches you the essential concepts of statistical learning and how to implement them using Python.

Unlock the power of data with this invaluable resource. "An Introduction to Statistical Learning" equips you with the necessary skills to analyze and interpret complex data sets. Whether you're in academia, industry, or any field that requires data-driven decision making, this book offers a wealth of knowledge to help you excel. With its clear explanations, hands-on examples, and step-by-step guidance, you'll quickly become proficient in various statistical learning techniques.

Not only does this book cover the fundamental concepts, but it also delves into advanced topics such as linear regression, classification, resampling methods, and tree-based methods. The inclusion of Python code throughout the book allows you to put theory into practice, reinforcing your understanding and facilitating experimentation. Gain valuable insights, make accurate predictions, and drive impactful results with the skills you'll develop from "An Introduction to Statistical Learning."

Don't miss out on this opportunity to advance your career and stay ahead of the competition. Order your copy of "An Introduction to Statistical Learning: with Applications in Python" today and take your data analysis skills to the next level. Learn from the experts in the field and become a sought-after data scientist. Get started by clicking here.

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