Discover Statistical Learning with Python: Embrace this Free Book

Discover Statistical Learning with Python: Embrace this Free Book

Introduction:

Introduction to Statistical Learning with Applications in R, also known as ISLR, has long been lauded as one of the best machine learning textbooks. Now, the Python edition of the book, ISL with Python, is here and the excitement in the community is palpable. The authors of this book excel at breaking down complex machine learning algorithms and their mathematical foundations in an easy-to-follow manner. However, the original ISLR examples and labs were in R, which posed a challenge for Python programmers. With the new Python edition, you can now code along and build machine learning models in Python, eliminating the need to learn a new programming language. This book covers all major areas of statistical learning, from linear regression and classification to deep learning and unsupervised learning. It also provides a Python programming crash course for beginners. The accompanying ISLP package makes the learning experience smoother by offering data loading utilities, documentation, and helper functions. Whether you are a beginner, a practitioner, or an educator, this book is a valuable resource for learning and implementing statistical learning in Python.

Full Article: Discover Statistical Learning with Python: Embrace this Free Book

Introduction to Statistical Learning with Applications in R, also known as ISLR, has long been regarded as one of the best machine learning textbooks for both beginners and practitioners. Now, with the release of the Python edition, Introduction to Statistical Learning with Applications in Python (ISL with Python), the machine learning community is even more excited.

The R version of the book has been widely praised for its ability to break down complex machine learning algorithms into easy-to-follow explanations, accompanied by the necessary mathematical foundations. However, the code examples and labs in ISLR are written in R, which posed a problem for those who were more comfortable programming in Python.

Fortunately, with the release of ISL with Python, this issue has been resolved. The Python edition allows readers to code along and build machine learning models in Python, removing the need to learn a new programming language. This makes it more accessible for those who are just starting out on their data journey.

In terms of content, the Python edition is similar to the R edition, but with appropriate adaptations for Python. The book covers a broad range of topics, including statistical learning, linear regression, classification, resampling methods, linear model selection and regularization, tree-based methods, support vector machines, deep learning, survival analysis and censored data, unsupervised learning, and multiple testing.

The book utilizes datasets sourced from publicly available repositories such as the UCI Machine Learning repository. These datasets cover various topics, such as bike sharing, credit card default, fund management, and crime rates. The ability to collect data from different sources and perform web scraping is essential for a data science project. However, for beginners, this step can introduce unnecessary complexity. To address this, the book comes with an accompanying ISLP package, which provides data loading utilities and simplifies the process.

The ISLP package is available for all major platforms and can be installed using pip. It also includes comprehensive documentation and helper functions to facilitate feature engineering and create higher-order features.

This book is suitable for beginners who prefer a self-taught approach to learning machine learning. It is also valuable for machine learning practitioners who want to revisit the basics and familiarize themselves with different algorithms. Additionally, educators can use this book as a primary resource for teaching machine learning courses.

The Python edition of Introduction to Statistical Learning is available for free online, but the paperback version sold out quickly on Amazon. Readers are encouraged to visit statlearning.com to start reading and exploring the Python edition of this highly anticipated book.

Summary: Discover Statistical Learning with Python: Embrace this Free Book

Introduction to Statistical Learning with Applications in Python, also known as ISL with Python, is the much-awaited Python edition of the popular machine learning textbook. This edition allows readers to build machine learning models using Python, eliminating the need to learn a new programming language. The book covers a wide range of topics, including statistical learning, linear regression, classification, deep learning, and more. It uses datasets from publicly available repositories and comes with an accompanying ISLP package, which provides useful functions and documentation. Whether you’re a beginner or an experienced practitioner, this book is a valuable resource in the field of machine learning. Start reading it today for a comprehensive learning experience.

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Data Science is an interdisciplinary field that combines scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It involves analyzing and interpreting large amounts of data to solve complex problems and make data-driven decisions.

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To become a successful Data Scientist, one should have a strong understanding of statistics, mathematics, and programming languages like Python or R. Additionally, skills in data visualization, machine learning, and domain knowledge are also crucial. Effective communication and problem-solving abilities are equally important.

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