![]() ![]() Note: Chapter-7,8,9 and 10 will be added soon. Dimension Reduction Methods- PCR and PLS Regression.Notebook: Chapter 6: Linear Model Selection and Regularization explains. Notebook: Chapter 5: Resampling Methods explains. Notebook: Chapter 4: Classification explains. Potential Problems with least square linear regression.Non-linear Transformations of the Predictors.Linear Regression (LR)- simple, multiple.Notebook: Chapter 3: Linear Regression explains. Introduction to Programming language, Python.Notebook: Chapter 2: Statistical Learning explains. If you want to quickly understand the book, learn statistical machine learning or/and python for data science, then just clone the repo and get started! □□Įxpect to learn following concepts & their implementation 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. So, I created a concise version of the book as a course on statistical machine learning in python. James, Robert Tibshirani, to learn the basics of statistics and machine learning models.Īnd understandably, completing a technical book while practicing it with relevant data and code is a challenge for lot of us. Whenever someone asks me “How to get started in data science?”, I usually recommend the book □ - Introduction to Statistical Learning by Daniela Witten, Trevor Hastie, Gareth M. Statistical Machine Learning in Python A summary of the book "Introduction to Statistical Learning" ![]()
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |