This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs.\nAs the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those ...
[051]6.Py Ridge Regression and the Lasso I 2023.zh_en 19:09 [052]7.1 Polynomials and Step Functions.zh_en 15:00 [053]7.2 Piecewise Polynomials and Splines.zh_en 13:14 [054]7.3 Smoothing Splines.zh_en 10:11 [055]7.4 Generalized Additive Models and Local Regression.zh_en ...
An Introduction to Statistical Learning: with Applications in Python (Gareth James, et al.) Lecture Slides, Videos, Interviews, etc. Book Homepage (R and Python Editions, Errata, Resources, etc.) Similar Books:
exploratory data analysis, and unsupervised learning. The second part on inferential data analysis covers linear and logistic regression and regularization. The last part studies machine learning with a focus on support-vector machines and deep learning. Each chapter is based on a dataset...
Python, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chatper. We also offer the separate and original version of this course called Statistical Learning with R – the chapter lectures are the same, but the lab lectures ...
Title: An Introduction to Statistical Learning: with Applications in R, 2nd Edition Author(s) Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Publisher: Springer; 2nd ed. (2021); eBook (Corrected Edition, June 21, 2023) Hardcover: 622 pages eBook: PDF (615 pages) Language:...
Python相关资料 An Introduction to Statistical Learning 下载积分: 700 内容提示: Springer Texts in StatisticsSeries Editors:G. CasellaS. FienbergI. OlkinFor further volumes:http://www.springer.com/series/417 文档格式:PDF | 页数:440 | 浏览次数:77 | 上传日期:2022-07-07 18:21:39 | 文档星级:...
GitHub的markdown公式支持一般,推荐使用Chrome插件TeX All the Things来渲染TeX公式,,本地Markdown编辑器推荐Typora,注意Ctrl+, 打开Preferences,Syntax Support部分勾选inline Math。Ubuntu和Windows都正常。 math_markdown.pdf为math_markdown.md的导出版本, 方便查看使用, markdown版本为最新版本,基本覆盖了书中用到的...
Each edition contains a lab at the end of each chapter, which demonstrates the chapter’s concepts in either R or Python. The chapters cover the following topics: What is statistical learning? Regression Classification Resampling methods Linear model selection and regularization ...
Python版本(ISLP)于2023年出版。 每个版本的书末都有一个实验室,展示了该章节中的概念在R或Python中的应用。 各章节涵盖以下主题: 什么是统计学习? 回归 分类 重采样方法 线性模型选择和正则化 超越线性 基于树的方法 支持向量机 深度学习 生存分析 无监督学习 多重检验 展开更多...