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Authors: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor
Publisher: Springer (2023) · Language: English
ISBN-10: 3031387465 · ISBN-13: 978-3031387463
An Introduction to Statistical Learning: with Applications in Python, 2023 Edition is the highly anticipated Python companion to one of the world’s most widely used texts in data science and applied statistics. Written by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor, this edition adapts the acclaimed ISL framework to the Python ecosystem while maintaining its hallmark clarity, accessibility, and rigor.
Designed for students, data scientists, and researchers, this edition covers the fundamental concepts of statistical learning, modeling, and data-driven prediction using modern Python tools and libraries.
Understand the relationship between statistical modeling, data analysis, and predictive performance.
Explore regression and classification models with practical examples and Python implementations.
Learn cross-validation, bootstrap techniques, and their applications in model validation and performance estimation.
Study ridge regression, lasso, and other methods to manage overfitting and improve generalization.
Discover splines, generalized additive models, decision trees, random forests, and boosting algorithms.
Delve into clustering, principal component analysis (PCA), and dimension reduction techniques.
Apply every concept through reproducible Python code examples, using libraries like NumPy, pandas, scikit-learn, and matplotlib.
Complete Python-based implementation of all major methods from the original R edition.
Modernized examples, datasets, and coding exercises aligned with real-world data science workflows.
Updated coverage of machine learning fundamentals and interpretability.
Enhanced visualizations, explanations, and end-of-chapter labs for self-guided learning.
Integration of reproducible Jupyter notebooks for hands-on practice.
An Introduction to Statistical Learning is celebrated for making complex statistical and machine learning methods approachable without sacrificing depth or mathematical rigor.
The Python edition bridges theory and practice, offering readers not only conceptual understanding but also practical coding experience—a vital skill in today’s data-driven fields. It serves as a gateway to more advanced works like The Elements of Statistical Learning while standing firmly as a complete, self-contained introduction.
Students and Instructors in statistics, data science, or machine learning courses.
Data Scientists and Analysts seeking a practical foundation in statistical modeling.
Researchers applying statistical learning methods to real-world data.
Professionals transitioning into data science or predictive analytics.
Statistical learning concepts and applications
Linear regression, classification, and resampling
Regularization and model selection
Nonlinear and tree-based methods
Unsupervised learning and PCA
Python implementations using scikit-learn and related libraries
Data visualization, evaluation, and interpretation
An Introduction to Statistical Learning: with Applications in Python, 2023 Edition delivers an ideal balance of theoretical insight and hands-on experience. Through its clear explanations, real-world examples, and Python-based workflow, it empowers readers to understand, build, and evaluate statistical and machine learning models with confidence.
Whether you are a student, educator, or professional, this edition stands as an essential resource for mastering the fundamentals of statistical learning in the modern programming environment.
