An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis. The first edition of this book, with applications in R (ISLR), was released in 2013. A 2nd Edition of ISLR was published in 2021.

This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided.

The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines ...

So, I created a concise version of the book as a course on statistical machine learning in python. In this repo, each chapter of the book has been translated into a jupyter notebook with summary of the key concepts, data & python code to play with.

Dec 16, 2024 · Let’s take a look at some of the key statistical concepts that are tightly related to machine learning. You can learn more about these concepts in our Statistics Fundamentals with Python skill track to gain hands-on experience.

“The book adopts a hands-on, practical approach to teaching statistical learning, featuring numerous examples and case studies, accompanied by Python code for implementation. It stands as a contemporary classic, offering clear and intuitive guidance on how to implement cutting-edge statistical and machine learning methods.

Dec 19, 2024 · Statistics and machine learning in Python have become important and a dominant tool for scientific computing, statistics, machine learning, and deep learning. With its extensive libraries and frameworks, Python allows developers, researchers, and data scientists to perform complex data analysis and build robust machine learning models efficiently.

5 days ago · Leverage your existing statistical knowledge Understanding your background helps you focus on what matters most for your goals. Even experienced developers should review Python fundamentals—recent versions have introduced significant improvements and new features worth mastering. ... Python dominates machine learning thanks to its rich ...

Univariate Statistics; Hands-On: Brain volumes study; Linear Mixed Models; Multivariate Statistics; Resampling and Monte Carlo Methods

Through hands-on exercises, you'll master essential techniques in regression, classification, and advanced algorithms in deep learning. Students will implement and test over 15 different machine learning methods, gaining practical experience through real-world case studies in finance, healthcare, ecommerce, and marketing and interactive ...