An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics) by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (ISBN 9783031391897)

An Introduction to Statistical Learning: with Applications in Python by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani is the long-awaited Python companion to one of the most widely used textbooks in data science and applied machine learning. Published by Springer, this 2024 edition brings the trusted clarity of ISLR to Python — featuring end-to-end examples, datasets, and code to make modern statistical learning accessible to students, analysts, and professionals.

Product Details

• Title: An Introduction to Statistical Learning: with Applications in Python
• Series: Springer Texts in Statistics
• Authors: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani
• Publisher: Springer International Publishing
• Publication Date: 2 July 2024
• ISBN-13: 9783031391897
• Format: Paperback / Academic Textbook
• Pages: 640 +
• Price: £44.99
• Availability: In Stock | Free UK & EU Shipping

This Python edition preserves the structure and depth of the original R-based ISLR while reworking every example and exercise in Python using libraries such as scikit-learn, pandas, numpy, and matplotlib. It covers the statistical and machine learning methods essential for modern data analysis — explained intuitively, with step-by-step coding demonstrations.

Key Features of the Python Edition

• All Examples in Python: Updated code written in scikit-learn and pandas for real-world application.
• Comprehensive Learning Path: Covers regression, classification, resampling, shrinkage methods, SVMs, trees, and unsupervised learning.
• Intuitive Explanations: Clear, non-technical explanations ideal for beginners and cross-disciplinary learners.
• Datasets & Code Repository: Includes open-access data and reproducible code on the official ISLR Python website.
• Bridges Theory and Practice: Links statistical reasoning with hands-on machine learning implementation.
• Authoritative Authors: Written by the creators of the Stanford “Statistical Learning” course — globally acclaimed in academia and industry.

Table of Contents Highlights

1. Introduction to Statistical Learning and Data Science
2. Linear Regression and Model Accuracy
3. Classification Techniques and Logistic Models
4. Resampling Methods and Cross-Validation
5. Shrinkage, Lasso, and Ridge Regression
6. Tree-Based Methods and Random Forests
7. Support Vector Machines and Kernel Methods
8. Unsupervised Learning and Clustering
9. Python Implementation and Practice Datasets

Who Should Use This Book?

• Students of statistics, computer science, and data analytics
• Machine learning engineers and data scientists
• Analysts transitioning from R to Python
• Researchers and educators teaching applied statistics

Why Buy from BooksGoat UK?

• Best Price: £44.99 vs RRP £59.99
• Free UK & EU Shipping
• Authentic Springer 2024 Edition verified ISBN 9783031391897
• Eco-Friendly Packaging and fast UK dispatch

FAQs

Is this the Python version of ISLR?

Yes — this 2024 edition reworks all examples from the R version using Python libraries.

Is it beginner-friendly?

Yes — designed for readers with minimal statistical background but interest in practical data science.

Are datasets and code freely available?

Yes — all datasets and Jupyter Notebook examples are available on the ISLR Python companion site.

Final Call – Buy Now

An Introduction to Statistical Learning: with Applications in Python by James, Witten, Hastie, and Tibshirani is the essential textbook for mastering modern machine learning techniques through Python. Order now from BooksGoat.co.uk for just £44.99. Free UK & EU shipping included.