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.
These labs will be useful both for Python novices, as well as experienced users..
Hence, this book (ISLP) covers the same materials as ISLR but With labs implemented in Python.
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.
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.
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.
This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge Statistical learning techniques to analyze their data.
Color graphics and real-world examples are used to illustrate the methods presented.
Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.
This book presents some of the most important modeling and prediction techniques, along With relevant applications.
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
