docs: make Chinese README the default

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-# Python Machine Learning book code repository
+<!-- WEHUB_ZH_README -->
 > [!NOTE]
 > 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。  
 > [English](./README.en.md) · [原始项目](https://github.com/rasbt/python-machine-learning-book) · [上游 README](https://github.com/rasbt/python-machine-learning-book/blob/HEAD/README.md)  
 > 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
 # Python Machine Learning 图书代码仓库
 [![Google Group](https://img.shields.io/badge/-Google%20Group-lightgrey.svg)](https://groups.google.com/forum/#!forum/python-machine-learning-reader-discussion-board)
 ---
-#### IMPORTANT NOTE (09/21/2017):
+#### 重要提示（2017/09/21）：
-This GitHub repository contains the code examples of the **1st Edition** of Python Machine Learning book. If you are looking for the code examples of the **2nd Edition**, please refer to [this](https://github.com/rasbt/python-machine-learning-book-2nd-edition#whats-new-in-the-second-edition-from-the-first-edition) repository instead. 
+本 GitHub 仓库包含《Python Machine Learning》**第 1 版**的代码示例。若你需要 **第 2 版**的代码示例，请参阅[此](https://github.com/rasbt/python-machine-learning-book-2nd-edition#whats-new-in-the-second-edition-from-the-first-edition) 仓库。
 ---
-What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and
+本书共 400 页，内容丰富实用，涵盖入门机器学习所需的大部分知识……从理论到可直接付诸实践的代码！这绝非又一本“scikit-learn 用法手册”。我的目标是解释所有底层概念，告诉你最佳实践与注意事项，并主要通过 NumPy、scikit-learn 和 Theano 将这些概念付诸实践。
 we will put those concepts into action mainly using NumPy, scikit-learn, and Theano.
-You are not sure if this book is for you? Please checkout the excerpts from the [Foreword](./docs/foreword_ro.pdf) and [Preface](./docs/preface_sr.pdf), or take a look at the [FAQ](#faq) section for further information.
+不确定这本书是否适合你？请查阅[序言](./docs/foreword_ro.pdf)和[前言](./docs/preface_sr.pdf)的节选，或查看 [FAQ](#faq) 部分了解更多信息。
@@ -22,10 +27,10 @@ You are not sure if this book is for you? Please checkout the excerpts from the
 [![](./images/pymle_cover_double_small.jpg)](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/ref=sr_1_1?ie=UTF8&qid=1470882464&sr=8-1&keywords=python+machine+learning)
-1st edition, published September 23rd 2015<br>
+第 1 版，2015 年 9 月 23 日出版<br>
-Paperback: 454 pages<br>
+平装：454 页<br>
-Publisher: Packt Publishing<br>  
+出版社：Packt Publishing<br>  
-Language: English<br>
+语言：英语<br>
 ISBN-10: 1783555130<br>  
 ISBN-13: 978-1783555130<br>
 Kindle ASIN: B00YSILNL0<br>
@@ -36,81 +41,80 @@ Kindle ASIN: B00YSILNL0<br>
 <br>
-German ISBN-13: 978-3958454224<br>
+德语 ISBN-13: 978-3958454224<br>
-Japanese ISBN-13: 978-4844380603<br>
+日语 ISBN-13: 978-4844380603<br>
-Italian ISBN-13: 978-8850333974<br>
+意大利语 ISBN-13: 978-8850333974<br>
-Chinese (traditional) ISBN-13: 978-9864341405<br>
+繁体中文 ISBN-13: 978-9864341405<br>
-Chinese (mainland) ISBN-13: 978-7111558804<br>
+大陆简体 ISBN-13: 978-7111558804<br>
-Korean ISBN-13: 979-1187497035<br>
+韩语 ISBN-13: 979-1187497035<br>
-Russian ISBN-13: 978-5970604090<br>
+俄语 ISBN-13: 978-5970604090<br>
-## Table of Contents and Code Notebooks
+## 目录与代码 Notebook
-Simply click on the `ipynb`/`nbviewer` links next to the chapter headlines to view the code examples (currently, the internal document links are only supported by the NbViewer version).
+只需点击各章标题旁的 `ipynb`/`nbviewer` 链接即可查看代码示例（目前，内部文档链接仅 NbViewer 版本支持）。
-**Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.**   
+**请注意，这些只是我为方便大家上传的随书代码示例；若无公式与说明文字，这些 notebook 可能用处不大。**   
- Excerpts from the [Foreword](./docs/foreword_ro.pdf) and [Preface](./docs/preface_sr.pdf)
+- [序言](./docs/foreword_ro.pdf)与[前言](./docs/preface_sr.pdf)节选
- [Instructions for setting up Python and the Jupiter Notebook](./code/ch01/README.md)  
+- [配置 Python 与 Jupiter Notebook 的说明](./code/ch01/README.md)  
 <br>
-1. Machine Learning - Giving Computers the Ability to Learn from Data [[dir](./code/ch01)] [[ipynb](./code/ch01/ch01.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb)]
+1. 机器学习——赋予计算机从数据中学习的能力 [[dir](./code/ch01)] [[ipynb](./code/ch01/ch01.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb)]
-2. Training Machine Learning Algorithms for Classification [[dir](./code/ch02)] [[ipynb](./code/ch02/ch02.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch02/ch02.ipynb)]
+2. 训练用于分类的机器学习算法 [[dir](./code/ch02)] [[ipynb](./code/ch02/ch02.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch02/ch02.ipynb)]
-3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[dir](./code/ch03)] [[ipynb](./code/ch03/ch03.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch03/ch03.ipynb)]
+3. 使用 Scikit-Learn 的机器学习分类器概览 [[dir](./code/ch03)] [[ipynb](./code/ch03/ch03.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch03/ch03.ipynb)]
-4. Building Good Training Sets – Data Pre-Processing [[dir](./code/ch04)] [[ipynb](./code/ch04/ch04.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch04/ch04.ipynb)]
+4. 构建优质训练集——数据预处理 [[dir](./code/ch04)] [[ipynb](./code/ch04/ch04.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch04/ch04.ipynb)]
-5. Compressing Data via Dimensionality Reduction [[dir](./code/ch05)] [[ipynb](./code/ch05/ch05.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch05/ch05.ipynb)]
+5. 通过降维压缩数据 [[dir](./code/ch05)] [[ipynb](./code/ch05/ch05.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch05/ch05.ipynb)]
-6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[dir](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch06/ch06.ipynb)]
+6. 学习模型评估与超参数调优的最佳实践 [[dir](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch06/ch06.ipynb)]
-7. Combining Different Models for Ensemble Learning [[dir](./code/ch07)] [[ipynb](./code/ch07/ch07.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch07/ch07.ipynb)]
+7. 组合不同模型进行集成学习 [[dir](./code/ch07)] [[ipynb](./code/ch07/ch07.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch07/ch07.ipynb)]
-8. Applying Machine Learning to Sentiment Analysis [[dir](./code/ch08)] [[ipynb](./code/ch08/ch08.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch08/ch08.ipynb)]
+8. 将机器学习应用于情感分析 [[dir](./code/ch08)] [[ipynb](./code/ch08/ch08.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch08/ch08.ipynb)]
-9. Embedding a Machine Learning Model into a Web Application [[dir](./code/ch09)] [[ipynb](./code/ch09/ch09.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch09/ch09.ipynb)]
+9. 将机器学习模型嵌入 Web 应用 [[dir](./code/ch09)] [[ipynb](./code/ch09/ch09.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch09/ch09.ipynb)]
-10. Predicting Continuous Target Variables with Regression Analysis [[dir](./code/ch10)] [[ipynb](./code/ch10/ch10.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch10/ch10.ipynb)]
+10. 用回归分析预测连续目标变量 [[dir](./code/ch10)] [[ipynb](./code/ch10/ch10.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch10/ch10.ipynb)]
-11. Working with Unlabeled Data – Clustering Analysis [[dir](./code/ch11)] [[ipynb](./code/ch11/ch11.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch11/ch11.ipynb)]
+11. 处理无标签数据——聚类分析 [[dir](./code/ch11)] [[ipynb](./code/ch11/ch11.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch11/ch11.ipynb)]
-12. Training Artificial Neural Networks for Image Recognition [[dir](./code/ch12)] [[ipynb](./code/ch12/ch12.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb)]
+12. 训练用于图像识别的人工神经网络 [[dir](./code/ch12)] [[ipynb](./code/ch12/ch12.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb)]
-13. Parallelizing Neural Network Training via Theano [[dir](./code/ch13)] [[ipynb](./code/ch13/ch13.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch13/ch13.ipynb)]
+13. 通过 Theano 并行化神经网络训练 [[dir](./code/ch13)] [[ipynb](./code/ch13/ch13.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch13/ch13.ipynb)]
 <br>
-#### Equation Reference
+#### 公式参考
 <a href="https://github.com/rasbt/python-machine-learning-book/tree/master/docs/equations"><img src="images/equation-ref-logo.png" width="200" height="200" /></a>
 [[PDF](./docs/equations/pymle-equations.pdf)] [[TEX](./docs/equations/pymle-equations.tex)]
-#### Slides for Teaching
+#### 教学幻灯片
-A big thanks to [Dmitriy Dligach](dmitriydligach) for sharing his slides from his machine learning course that is currently offered at [Loyola University Chicago](http://www.luc.edu/cs/). 
+衷心感谢 [Dmitriy Dligach](dmitriydligach) 分享其机器学习课程幻灯片，该课程目前在 [Loyola University Chicago](http://www.luc.edu/cs/). 开设。
 - [https://github.com/dmitriydligach/PyMLSlides](https://github.com/dmitriydligach/PyMLSlides)
 - 
-#### Additional Math and NumPy Resources
+#### 补充数学与 NumPy 资源
-Some readers were asking about Math and NumPy primers, since they were not included due to length limitations. However, I recently put together such resources for another book, but I made these *chapters* freely available online in hope that they also serve as helpful background material for this book:
+一些读者询问数学与 NumPy 入门材料，因篇幅限制未收入本书。不过我最近为另一本书整理了此类资源，并自愿将这些*章节*免费在线提供，希望也能作为本书的有益背景材料：
- Algebra Basics [[PDF](https://sebastianraschka.com/pdf/books/dlb/appendix_b_algebra.pdf)] [[EPUB](https://sebastianraschka.com/pdf/books/dlb/appendix_b_algebra.epub)]
+- 代数基础 [[PDF](https://sebastianraschka.com/pdf/books/dlb/appendix_b_algebra.pdf)] [[EPUB](https://sebastianraschka.com/pdf/books/dlb/appendix_b_algebra.epub)]
- A Calculus and Differentiation Primer [[PDF](https://sebastianraschka.com/pdf/books/dlb/appendix_d_calculus.pdf)] [[EPUB](https://sebastianraschka.com/pdf/books/dlb/appendix_d_calculus.epub)]
+- 微积分与微分入门 [[PDF](https://sebastianraschka.com/pdf/books/dlb/appendix_d_calculus.pdf)] [[EPUB](https://sebastianraschka.com/pdf/books/dlb/appendix_d_calculus.epub)]
- Introduction to NumPy [[PDF](https://sebastianraschka.com/pdf/books/dlb/appendix_f_numpy-intro.pdf)] [[EPUB](https://sebastianraschka.com/pdf/books/dlb/appendix_f_numpy-intro.epub)] [[Code Notebook](https://github.com/rasbt/deep-learning-book/blob/master/code/appendix_f_numpy-intro/appendix_f_numpy-intro.ipynb)]
+- NumPy 入门 [[PDF](https://sebastianraschka.com/pdf/books/dlb/appendix_f_numpy-intro.pdf)] [[EPUB](https://sebastianraschka.com/pdf/books/dlb/appendix_f_numpy-intro.epub)] [[Code Notebook](https://github.com/rasbt/deep-learning-book/blob/master/code/appendix_f_numpy-intro/appendix_f_numpy-intro.ipynb)]
 ---
-#### Citing this Book
+#### 引用本书
-You are very welcome to re-use the code snippets or other contents from this book
+非常欢迎在科学出版物及其他作品中复用本书代码片段或其他内容；
-in scientific publications and other works;
+若如此，恳请引用原始来源：
 in this case, I would appreciate citations to the original source:
-**BibTeX**:
+**BibTeX**：
 ```
@Book{raschka2015python,
@@ -124,74 +128,74 @@ in this case, I would appreciate citations to the original source:
 ```
-**MLA**:
+**MLA**：
 Raschka, Sebastian. *Python machine learning*. Birmingham, UK: Packt Publishing, 2015. Print.
 ---
-### [Feedback & Reviews](./docs/feedback.md)
+### [反馈与评论](./docs/feedback.md)
-#### [Short review snippets](./docs/feedback.md)
+#### [简短书评摘录](./docs/feedback.md)
 [![](./images/pymle_amzn.png)](https://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/ref=sr_1_1?ie=UTF8&qid=1472342570&sr=8-1&keywords=sebastian+raschka)
 ---
-> *Sebastian Raschka’s new book, Python Machine Learning, has just been released. I got a chance to read a review copy and it’s just as I expected - really great! It’s well organized, super easy to follow, and it not only offers a good foundation for smart, non-experts, practitioners will get some ideas and learn new tricks here as well.*  
+> *Sebastian Raschka 的新书《Python Machine Learning》刚刚出版。我有机会阅读了审阅版，结果正如我所料——非常棒！结构清晰、极易上手，不仅为聪明的非专业人士打下了良好基础，从业者也能从中获得灵感并学到新技巧。*  
-– Lon Riesberg at [Data Elixir](http://dataelixir.com/issues/55#start)
+– Lon Riesberg，[Data Elixir](http://dataelixir.com/issues/55#start)
-> *Superb job! Thus far, for me it seems to have hit the right balance of theory and practice…math and code!*   
+> *出色的工作！到目前为止，在我看来它在理论与实践的把握上恰到好处……数学与代码兼顾！*   
 – [Brian Thomas](http://sebastianraschka.com/blog/2015/writing-pymle.html#comment-2295668894)
-> *I've read (virtually) every Machine Learning title based around Scikit-learn and this is hands-down the best one out there.*    
+> *我（几乎）读过所有围绕 Scikit-learn 的机器学习书籍，这本无疑是其中最好的一本。*    
 – [Jason Wolosonovich](https://www.linkedin.com/pulse/python-machine-learning-sebastian-raschka-review-jason-wolosonovich?trk=prof-post)
-> *The best book I've seen to come out of PACKT Publishing. This is a very well written introduction to machine learning with Python. As others have noted, a perfect mixture of theory and application.*    
+> *这是我见过的 PACKT Publishing 出品的最佳图书。这是一本写得非常好的 Python 机器学习入门书。正如其他人所指出的，理论与应用的结合堪称完美。*    
 – [Josh D.](https://www.amazon.com/gp/customer-reviews/R27WB1GWTNGIR2/ref=cm_cr_getr_d_rvw_ttl?ie=UTF8&ASIN=1783555130)
-> *A book with a blend of qualities that is hard to come by: combines the needed mathematics to control the theory with the applied coding in Python. Also great to see it doesn't waste paper in giving a primer on Python as many other books do just to appeal to the greater audience. You can tell it's been written by knowledgeable writers and not just DIY geeks.*    
+> *一本很难得的、兼具多种优点的书：将掌控理论所需的数学与应用性的 Python 编码结合起来。也很高兴看到它没有像许多其他书那样，为了迎合更广泛的读者而浪费篇幅去介绍 Python 入门。可以看出它是由有见地的作者所写，而不只是 DIY 爱好者。*    
 – [Amazon Customer](https://www.amazon.com/gp/customer-reviews/RZWY4TF66Z6V0/ref=cm_cr_getr_d_rvw_ttl?ie=UTF8&ASIN=1783555130)
-> *Sebastian Raschka created an amazing machine learning tutorial which combines theory with practice. The book explains machine learning from a theoretical perspective and has tons of coded examples to show how you would actually use the machine learning technique. It can be read by a beginner or advanced programmer.*
+> *Sebastian Raschka 创作了一份精彩的机器学习教程，将理论与实践相结合。该书从理论角度解释机器学习，并有大量代码示例展示如何实际运用机器学习技术。初学者或高级程序员都可以阅读。*
- William P. Ross, [7 Must Read Python Books](http://williampross.com/7-must-read-python-books/)
+- William P. Ross，[7 Must Read Python Books](http://williampross.com/7-must-read-python-books/)
-#### Longer reviews
+#### 较长书评
-If you need help to decide whether this book is for you, check out some of the "longer" reviews linked below. (If you wrote a review, please let me know, and I'd be happy to add it to the list).
+如果你需要帮忙判断是否适合阅读本书，可以查看下方链接中的一些“较长”书评。（如果你写过书评，请告诉我，我很乐意将其加入列表。）
- [Python Machine Learning Review](http://www.bcs.org/content/conWebDoc/55586) by Patrick Hill at the Chartered Institute for IT
+- [Python Machine Learning Review](http://www.bcs.org/content/conWebDoc/55586)，作者 Patrick Hill，发表于英国特许信息技术学会（Chartered Institute for IT）
- [Book Review: Python Machine Learning by Sebastian Raschka](http://whatpixel.com/python-machine-learning-book-review/) by Alex Turner at WhatPixel
+- [Book Review: Python Machine Learning by Sebastian Raschka](http://whatpixel.com/python-machine-learning-book-review/)，作者 Alex Turner，发表于 WhatPixel
 ---
-## Links
+## 链接
- ebook and paperback at [Amazon.com](http://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/ref=sr_1_2?ie=UTF8&qid=1437754343&sr=8-2&keywords=python+machine+learning+essentials), [Amazon.co.uk](http://www.amazon.co.uk/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130), [Amazon.de](http://www.amazon.de/s/ref=nb_sb_noss_2?__mk_de_DE=ÅMÅŽÕÑ&url=search-alias%3Daps&field-keywords=python+machine+learning)
+- 电子书与纸质书：[Amazon.com](http://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130/ref=sr_1_2?ie=UTF8&qid=1437754343&sr=8-2&keywords=python+machine+learning+essentials), [Amazon.co.uk](http://www.amazon.co.uk/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130), [Amazon.de](http://www.amazon.de/s/ref=nb_sb_noss_2?__mk_de_DE=ÅMÅŽÕÑ&url=search-alias%3Daps&field-keywords=python+machine+learning)
- [ebook and paperback](https://www.packtpub.com/big-data-and-business-intelligence/python-machine-learning) from Packt (the publisher)
+- [电子书与纸质书](https://www.packtpub.com/big-data-and-business-intelligence/python-machine-learning)，来自出版社 Packt
- at other book stores: [Google Books](https://books.google.com/books?id=GOVOCwAAQBAJ&source=gbs_slider_cls_metadata_7_mylibrary), [O'Reilly](http://shop.oreilly.com/product/9781783555130.do), [Safari](https://www.safaribooksonline.com/library/view/python-machine-learning/9781783555130/), [Barnes & Noble](http://www.barnesandnoble.com/w/python-machine-learning-essentials-sebastian-raschka/1121999969?ean=9781783555130), [Apple iBooks](https://itunes.apple.com/us/book/python-machine-learning/id1028207310?mt=11), ...
+- 其他书店：[Google Books](https://books.google.com/books?id=GOVOCwAAQBAJ&source=gbs_slider_cls_metadata_7_mylibrary), [O'Reilly](http://shop.oreilly.com/product/9781783555130.do), [Safari](https://www.safaribooksonline.com/library/view/python-machine-learning/9781783555130/), [Barnes & Noble](http://www.barnesandnoble.com/w/python-machine-learning-essentials-sebastian-raschka/1121999969?ean=9781783555130), [Apple iBooks](https://itunes.apple.com/us/book/python-machine-learning/id1028207310?mt=11), ...
- social platforms: [Goodreads](https://www.goodreads.com/book/show/25545994-python-machine-learning)
+- 社交平台：[Goodreads](https://www.goodreads.com/book/show/25545994-python-machine-learning)
-#### Translations
+#### 译本
- [Italian translation](https://www.amazon.it/learning-Costruire-algoritmi-generare-conoscenza/dp/8850333978/) via "Apogeo"
+- [意大利语译本](https://www.amazon.it/learning-Costruire-algoritmi-generare-conoscenza/dp/8850333978/)，经 "Apogeo" 出版
- [German translation](https://www.amazon.de/Machine-Learning-Python-mitp-Professional/dp/3958454224/) via "mitp Verlag"
+- [德语译本](https://www.amazon.de/Machine-Learning-Python-mitp-Professional/dp/3958454224/)，经 "mitp Verlag" 出版
- [Japanese translation](http://www.amazon.co.jp/gp/product/4844380605/) via "Impress Top Gear"
+- [日语译本](http://www.amazon.co.jp/gp/product/4844380605/)，经 "Impress Top Gear" 出版
- [Chinese translation (traditional Chinese)](https://taiwan.kinokuniya.com/bw/9789864341405)
+- [中文译本（繁体中文）](https://taiwan.kinokuniya.com/bw/9789864341405)
- [Chinese translation (simple Chinese)](https://book.douban.com/subject/27000110/)
+- [中文译本（简体中文）](https://book.douban.com/subject/27000110/)
- [Korean translation](http://www.kyobobook.co.kr/product/detailViewKor.laf?mallGb=KOR&ejkGb=KOR&barcode=9791187497035) via "Kyobo"
+- [韩语译本](http://www.kyobobook.co.kr/product/detailViewKor.laf?mallGb=KOR&ejkGb=KOR&barcode=9791187497035)，经 "Kyobo" 出版
- [Polish translation](https://www.amazon.de/Python-Uczenie-maszynowe-Sebastian-Raschka/dp/8328336138/ref=sr_1_11?ie=UTF8&qid=1513601461&sr=8-11&keywords=sebastian+raschka) via "Helion"
+- [波兰语译本](https://www.amazon.de/Python-Uczenie-maszynowe-Sebastian-Raschka/dp/8328336138/ref=sr_1_11?ie=UTF8&qid=1513601461&sr=8-11&keywords=sebastian+raschka)，经 "Helion" 出版
 ---
-### [Literature References & Further Reading Resources](./docs/references.md)
+### [文献参考与延伸阅读资源](./docs/references.md)
-### [Errata](./docs/errata.md)
+### [勘误](./docs/errata.md)
 ---
-### Bonus Notebooks (not in the book)
+### 附赠 Notebook（书中未收录）
 - Logistic Regression Implementation [[dir](./code/bonus)] [[ipynb](./code/bonus/logistic_regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/logistic_regression.ipynb)]
 - A Basic Pipeline and Grid Search Setup [[dir](./code/bonus)] [[ipynb](./code/bonus/svm_iris_pipeline_and_gridsearch.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/svm_iris_pipeline_and_gridsearch.ipynb)]
@@ -203,7 +207,7 @@ If you need help to decide whether this book is for you, check out some of the "
 <hr>
-**"Related Content" (not in the book)**
+**"相关内容"（书中未收录）**
 - [Model evaluation, model selection, and algorithm selection in machine learning - Part I](http://sebastianraschka.com/blog/2016/model-evaluation-selection-part1.html)
 - [Model evaluation, model selection, and algorithm selection in machine learning - Part II](http://sebastianraschka.com/blog/2016/model-evaluation-selection-part2.html)
@@ -213,20 +217,20 @@ If you need help to decide whether this book is for you, check out some of the "
 #### SciPy 2016
-We had such a great time at [SciPy 2016](http://scipy2016.scipy.org/ehome/index.php?eventid=146062&tabid=332930&) in Austin! It was a real pleasure to meet and chat with so many readers of my book. Thanks so much for all the nice words and feedback! And in case you missed it, Andreas Mueller and I gave an **Introduction to Machine Learning with Scikit-learn**; if you are interested, the video recordings of [Part I](https://www.youtube.com/watch?v=OB1reY6IX-o&index=91&list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6) and [Part II](https://www.youtube.com/watch?v=Cte8FYCpylk&list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6&index=90) are now online!
+我们在奥斯汀参加的 [SciPy 2016](http://scipy2016.scipy.org/ehome/index.php?eventid=146062&tabid=332930&) 度过了非常愉快的时光！能与这么多本书的读者见面交流，真是莫大的荣幸。非常感谢大家的赞誉与反馈！如果你错过了，Andreas Mueller 和我曾做过一场 **Introduction to Machine Learning with Scikit-learn**；如果你感兴趣，[Part I](https://www.youtube.com/watch?v=OB1reY6IX-o&index=91&list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6) 和 [Part II](https://www.youtube.com/watch?v=Cte8FYCpylk&list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6&index=90) 的视频录像现已上线！
 [![](images/scipy2016.jpg)](https://www.youtube.com/watch?v=OB1reY6IX-o&index=91&list=PLYx7XA2nY5Gf37zYZMw6OqGFRPjB1jCy6)
 #### PyData Chicago 2016
-I attempted the rather challenging task of introducing scikit-learn & machine learning in *just* 90 minutes at PyData Chicago 2016. The slides and tutorial material are available at "[Learning scikit-learn -- An Introduction to Machine Learning in Python](https://github.com/rasbt/pydata-chicago2016-ml-tutorial)."
+我曾在 PyData Chicago 2016 上尝试完成一项颇具挑战的任务：在*仅仅* 90 分钟内介绍 scikit-learn 与机器学习（machine learning）。幻灯片与教程材料可在「[Learning scikit-learn -- An Introduction to Machine Learning in Python](https://github.com/rasbt/pydata-chicago2016-ml-tutorial).」获取。
 ---
-**Note**
+**说明**
-I have set up a separate library, [`mlxtend`](http://rasbt.github.io/mlxtend/), containing additional implementations of machine learning (and general "data science") algorithms. I also added implementations from this book (for example, the decision region plot, the artificial neural network, and sequential feature selection algorithms) with additional functionality.
+我另外搭建了一个独立库 [`mlxtend`](http://rasbt.github.io/mlxtend/),，其中包含更多机器学习（以及一般意义上的「数据科学」）算法的实现。我还加入了本书中的若干实现（例如决策区域图、人工神经网络，以及序列特征选择算法），并补充了额外功能。
 [![](./images/mlxtend_logo.png)](http://rasbt.github.io/mlxtend/)
@@ -235,7 +239,7 @@ I have set up a separate library, [`mlxtend`](http://rasbt.github.io/mlxtend/),
 <hr>
-### Translations
+### 译本
 [![](./images/pymle-cover_it.jpg)](https://www.amazon.it/learning-Costruire-algoritmi-generare-conoscenza/dp/8850333978/)
 [![](./images/pymle-cover_de.jpg)](https://www.amazon.de/Machine-Learning-Python-mitp-Professional/dp/3958454224/)
@@ -250,167 +254,165 @@ I have set up a separate library, [`mlxtend`](http://rasbt.github.io/mlxtend/),
 ---
-***Dear readers***,  
+***亲爱的读者们***，  
-first of all, I want to thank all of you for the great support! I am really happy about all the great feedback you sent me so far, and I am glad that the book has been so useful to a broad audience.
+首先，我要感谢大家对本书的大力支持！我非常高兴收到你们迄今为止发来的诸多精彩反馈，也很欣慰这本书能对广大读者如此有用。
-Over the last couple of months, I received hundreds of emails, and I tried to answer as many as possible in the available time I have. To make them useful to other readers as well, I collected many of my answers in the FAQ section (below).
+过去几个月里，我收到了数百封邮件，并尽力在有限时间内尽可能多地回复。为了让这些回答也能惠及其他读者，我将其中许多内容整理进了下方的 FAQ 部分。
-In addition, some of you asked me about a platform for readers to discuss the contents of the book. I hope that this would provide an opportunity for you to discuss and share your knowledge with other readers:
+此外，有些人询问是否有供读者讨论本书内容的平台。我希望这能为大家提供一个与其他读者交流、分享知识的机会：
-#### [Google Groups Discussion Board](https://groups.google.com/forum/#!forum/python-machine-learning-reader-discussion-board)
+#### [Google Groups 讨论区](https://groups.google.com/forum/#!forum/python-machine-learning-reader-discussion-board)
-(And I will try my best to answer questions myself if time allows! :))
+（若时间允许，我也会尽力亲自回答问题！:））
-> The only thing to do with good advice is to pass it on. It is never of any use to oneself.  
+> 对于好建议，唯一该做的事就是把它传递下去。它对自己从来毫无用处。  
 — Oscar Wilde
 ---
-## Examples and Applications by Readers
+## 读者示例与应用
-Once again, I have to say (big!) THANKS for all the nice feedback about the book. I've received many emails from readers, who
+再次向大家对本书的积极反馈致以（大大的！）感谢。我收到许多读者来信，他们将本书的概念与示例付诸实践，并在各自项目中善加运用。在本节中，我开始收集其中一些精彩应用；若你愿意，我很乐意把你的项目也加入这份列表——发封简短邮件即可！
 put the concepts and examples from this book out into the real world and make good use of them in their projects. In this section, I am
 starting to gather some of these great applications, and I'd be more than happy to add your project to this list -- just shoot me a quick mail!
- [40 scripts on Optical Character Recognition](https://github.com/rrlyman/PythonMachineLearingExamples) by [Richard Lyman](https://github.com/rrlyman)
+- [40 个光学字符识别（Optical Character Recognition）脚本](https://github.com/rrlyman/PythonMachineLearingExamples)，作者 [Richard Lyman](https://github.com/rrlyman)
- [Code experiments](https://github.com/jeremyn/python-machine-learning-book) by [Jeremy Nation](https://github.com/jeremyn)
+- [代码实验](https://github.com/jeremyn/python-machine-learning-book)，作者 [Jeremy Nation](https://github.com/jeremyn)
- [What I Learned Implementing a Classifier from Scratch in Python](http://www.jeannicholashould.com) by [Jean-Nicholas Hould](http://www.jeannicholashould.com)
+- [从零用 Python 实现分类器，我学到了什么](http://www.jeannicholashould.com)，作者 [Jean-Nicholas Hould](http://www.jeannicholashould.com)
 ## FAQ
-### General Questions
+### 一般问题
- [What are machine learning and data science?](./faq/datascience-ml.md)
+- [什么是机器学习与数据科学？](./faq/datascience-ml.md)
- [Why do you and other people sometimes implement machine learning algorithms from scratch?](./faq/implementing-from-scratch.md)
+- [为什么你和其他人有时会从零实现机器学习算法？](./faq/implementing-from-scratch.md)
- [What learning path/discipline in data science I should focus on?](./faq/data-science-career.md)
+- [在数据科学中，我应专注于哪条学习路径/学科方向？](./faq/data-science-career.md)
- [At what point should one start contributing to open source?](./faq/open-source.md)
+- [应在何时开始为开源项目做贡献？](./faq/open-source.md)
- [How important do you think having a mentor is to the learning process?](./faq/mentor.md)
+- [你认为拥有导师对学习过程有多重要？](./faq/mentor.md)
- [Where are the best online communities centered around data science/machine learning or python?](./faq/ml-python-communities.md)
+- [围绕数据科学/机器学习或 Python 的最佳在线社区有哪些？](./faq/ml-python-communities.md)
- [How would you explain machine learning to a software engineer?](./faq/ml-to-a-programmer.md)
+- [你会如何向软件工程师解释机器学习？](./faq/ml-to-a-programmer.md)
- [How would your curriculum for a machine learning beginner look like?](./faq/ml-curriculum.md)
+- [你为机器学习初学者设计的课程大纲会是什么样？](./faq/ml-curriculum.md)
- [What is the Definition of Data Science?](./faq/definition_data-science.md)
+- [数据科学的定义是什么？](./faq/definition_data-science.md)
- [How do Data Scientists perform model selection? Is it different from Kaggle?](./faq/model-selection-in-datascience.md)
+- [数据科学家如何进行模型选择？这与 Kaggle 有何不同？](./faq/model-selection-in-datascience.md)
-### Questions about the Machine Learning Field
+### 关于机器学习领域的问题
- [How are Artificial Intelligence and Machine Learning related?](./faq/ai-and-ml.md)
+- [人工智能（Artificial Intelligence）与机器学习有何关联？](./faq/ai-and-ml.md)
- [What are some real-world examples of applications of machine learning in the field?](./faq/ml-examples.md)
+- [机器学习在该领域有哪些真实世界应用示例？](./faq/ml-examples.md)
- [What are the different fields of study in data mining?](./faq/datamining-overview.md)
+- [数据挖掘（data mining）包含哪些不同的研究领域？](./faq/datamining-overview.md)
- [What are differences in research nature between the two fields: machine learning & data mining?](./faq/datamining-vs-ml.md)
+- [机器学习与数据挖掘这两个领域的研究性质有何差异？](./faq/datamining-vs-ml.md)
- [How do I know if the problem is solvable through machine learning?](./faq/ml-solvable.md)
+- [如何判断一个问题能否通过机器学习解决？](./faq/ml-solvable.md)
- [What are the origins of machine learning?](./faq/ml-origins.md)
+- [机器学习的起源是什么？](./faq/ml-origins.md)
- [How was classification, as a learning machine, developed?](./faq/classifier-history.md)
+- [分类器作为学习机器是如何发展起来的？](./faq/classifier-history.md)
- [Which machine learning algorithms can be considered as among the best?](./faq/best-ml-algo.md)
+- [哪些机器学习算法可被视为最佳之列？](./faq/best-ml-algo.md)
- [What are the broad categories of classifiers?](./faq/classifier-categories.md)
+- [分类器有哪些大类？](./faq/classifier-categories.md)
- [What is the difference between a classifier and a model?](./faq/difference_classifier_model.md)
+- [分类器（classifier）与模型（model）有何区别？](./faq/difference_classifier_model.md)
- [What is the difference between a parametric learning algorithm and a nonparametric learning algorithm?](./faq/parametric_vs_nonparametric.md)
+- [参数学习算法（parametric learning algorithm）与非参数学习算法（nonparametric learning algorithm）有何区别？](./faq/parametric_vs_nonparametric.md)
- [What is the difference between a cost function and a loss function in machine learning?](./faq/cost-vs-loss.md)
+- [机器学习中代价函数（cost function）与损失函数（loss function）有何区别？](./faq/cost-vs-loss.md)
-### Questions about ML Concepts and Statistics
+### 关于机器学习概念与统计学的问题
-##### Cost Functions and Optimization
+##### 代价函数与优化
- [Fitting a model via closed-form equations vs. Gradient Descent vs Stochastic Gradient Descent vs Mini-Batch Learning -- what is the difference?](./faq/closed-form-vs-gd.md)
+- [通过闭式方程拟合模型 vs. 梯度下降（Gradient Descent）vs 随机梯度下降（Stochastic Gradient Descent）vs 小批量学习（Mini-Batch Learning）——有何区别？](./faq/closed-form-vs-gd.md)
- [How do you derive the Gradient Descent rule for Linear Regression and Adaline?](./faq/linear-gradient-derivative.md)
+- [如何推导线性回归（Linear Regression）与 Adaline 的梯度下降规则？](./faq/linear-gradient-derivative.md)
-##### Regression Analysis
+##### 回归分析
- [What is the difference between Pearson R and Simple Linear Regression?](./faq/pearson-r-vs-linear-regr.md)
+- [Pearson R 与简单线性回归（Simple Linear Regression）有何区别？](./faq/pearson-r-vs-linear-regr.md)
-##### Tree models
+##### 树模型
- [How does the random forest model work? How is it different from bagging and boosting in ensemble models?](./faq/bagging-boosting-rf.md)
+- [随机森林（random forest）模型如何工作？它与集成模型中的 bagging 和 boosting 有何不同？](./faq/bagging-boosting-rf.md)
- [What are the disadvantages of using classic decision tree algorithm for a large dataset?](./faq/decision-tree-disadvantages.md)
+- [对大型数据集使用经典决策树算法有哪些缺点？](./faq/decision-tree-disadvantages.md)
- [Why are implementations of decision tree algorithms usually binary, and what are the advantages of the different impurity metrics?](./faq/decision-tree-binary.md)
+- [为什么决策树算法的实现通常是二分的？不同不纯度指标各有何优势？](./faq/decision-tree-binary.md)
- [Why are we growing decision trees via entropy instead of the classification error?](./faq/decisiontree-error-vs-entropy.md)
+- [为什么我们通过熵（entropy）而非分类误差来生长决策树？](./faq/decisiontree-error-vs-entropy.md)
- [When can a random forest perform terribly?](./faq/random-forest-perform-terribly.md)
+- [随机森林何时可能表现极差？](./faq/random-forest-perform-terribly.md)
-##### Model evaluation
+##### 模型评估
- [What is overfitting?](./faq/overfitting.md)
+- [什么是过拟合（overfitting）？](./faq/overfitting.md)
- [How can I avoid overfitting?](./faq/avoid-overfitting.md)
+- [如何避免过拟合？](./faq/avoid-overfitting.md)
- [Is it always better to have the largest possible number of folds when performing cross validation?](./faq/number-of-kfolds.md)
+- [进行交叉验证（cross validation）时，折数（fold）是否越多越好？](./faq/number-of-kfolds.md)
- [When training an SVM classifier, is it better to have a large or small number of support vectors?](./faq/num-support-vectors.md)
+- [训练 SVM 分类器时，支持向量（support vector）数量多些好还是少些好？](./faq/num-support-vectors.md)
- [How do I evaluate a model?](./faq/evaluate-a-model.md)
+- [如何评估模型？](./faq/evaluate-a-model.md)
- [What is the best validation metric for multi-class classification?](./faq/multiclass-metric.md)
+- [多类分类（multi-class classification）的最佳验证指标是什么？](./faq/multiclass-metric.md)
- [What factors should I consider when choosing a predictive model technique?](./faq/choosing-technique.md)
+- [选择预测建模技术时应考虑哪些因素？](./faq/choosing-technique.md)
- [What are the best toy datasets to help visualize and understand classifier behavior?](./faq/clf-behavior-data.md)
+- [哪些最佳玩具数据集有助于可视化并理解分类器行为？](./faq/clf-behavior-data.md)
- [How do I select SVM kernels?](./faq/select_svm_kernels.md)
+- [如何选择 SVM 核函数（kernel）？](./faq/select_svm_kernels.md)
- [Interlude: Comparing and Computing Performance Metrics in Cross-Validation -- Imbalanced Class Problems and 3 Different Ways to Compute the F1 Score](./faq/computing-the-f1-score.md)
+- [插曲：在交叉验证中比较与计算性能指标——类别不平衡问题与计算 F1 分数的 3 种不同方法](./faq/computing-the-f1-score.md)
-##### Logistic Regression
+##### 逻辑回归（Logistic Regression）
- [What is Softmax regression and how is it related to Logistic regression?](./faq/softmax_regression.md)
+- [什么是 Softmax 回归？它与逻辑回归有何关系？](./faq/softmax_regression.md)
- [Why is logistic regression considered a linear model?](./faq/logistic_regression_linear.md)
+- [为什么逻辑回归被视为线性模型？](./faq/logistic_regression_linear.md)
- [What is the probabilistic interpretation of regularized logistic regression?](./faq/probablistic-logistic-regression.md)
+- [正则化逻辑回归的概率解释是什么？](./faq/probablistic-logistic-regression.md)
- [Does regularization in logistic regression always results in better fit and better generalization?](./faq/regularized-logistic-regression-performance.md)
+- [逻辑回归中的正则化是否总能带来更好的拟合和更好的泛化？](./faq/regularized-logistic-regression-performance.md)
- [What is the major difference between naive Bayes and logistic regression?](./faq/naive-bayes-vs-logistic-regression.md)
+- [朴素贝叶斯（Naive Bayes）与逻辑回归的主要区别是什么？](./faq/naive-bayes-vs-logistic-regression.md)
- [What exactly is the "softmax and the multinomial logistic loss" in the context of machine learning?](./faq/softmax.md)
+- [在机器学习语境下，"softmax 与多项逻辑损失（multinomial logistic loss）"究竟指什么？](./faq/softmax.md)
- [What is the relation between Logistic Regression and Neural Networks and when to use which?](./faq/logisticregr-neuralnet.md)
+- [逻辑回归与神经网络有何关系？应何时选用哪一种？](./faq/logisticregr-neuralnet.md)
- [Logistic Regression: Why sigmoid function?](./faq/logistic-why-sigmoid.md)
+- [逻辑回归：为何使用 sigmoid 函数？](./faq/logistic-why-sigmoid.md)
- [Is there an analytical solution to Logistic Regression similar to the Normal Equation for Linear Regression?](./faq/logistic-analytical.md)
+- [逻辑回归是否存在类似线性回归正规方程（Normal Equation）的解析解？](./faq/logistic-analytical.md)
-##### Neural Networks and Deep Learning
+##### 神经网络与深度学习（Neural Networks and Deep Learning）
- [What is the difference between deep learning and usual machine learning?](./faq/difference-deep-and-normal-learning.md)
+- [深度学习与常规机器学习有何区别？](./faq/difference-deep-and-normal-learning.md)
- [Can you give a visual explanation for the back propagation algorithm for neural networks?](./faq/visual-backpropagation.md)
+- [能否用可视化方式解释神经网络的反向传播（back propagation）算法？](./faq/visual-backpropagation.md)
- [Why did it take so long for deep networks to be invented?](./faq/inventing-deeplearning.md)
+- [为何深度网络的发明花了这么长时间？](./faq/inventing-deeplearning.md)
- [What are some good books/papers for learning deep learning?](./faq/deep-learning-resources.md)
+- [学习深度学习有哪些好书/论文？](./faq/deep-learning-resources.md)
- [Why are there so many deep learning libraries?](./faq/many-deeplearning-libs.md)
+- [为何深度学习库如此之多？](./faq/many-deeplearning-libs.md)
- [Why do some people hate neural networks/deep learning?](./faq/deeplearning-criticism.md)
+- [为何有人反感神经网络/深度学习？](./faq/deeplearning-criticism.md)
- [How can I know if Deep Learning works better for a specific problem than SVM or random forest?](./faq/deeplearn-vs-svm-randomforest.md)
+- [如何判断深度学习是否比 SVM 或随机森林更适合某个具体问题？](./faq/deeplearn-vs-svm-randomforest.md)
- [What is wrong when my neural network's error increases?](./faq/neuralnet-error.md)
+- [神经网络误差上升时问题出在哪里？](./faq/neuralnet-error.md)
- [How do I debug an artificial neural network algorithm?](./faq/nnet-debugging-checklist.md)
+- [如何调试人工神经网络算法？](./faq/nnet-debugging-checklist.md)
- [What is the difference between a Perceptron, Adaline, and neural network model?](./faq/diff-perceptron-adaline-neuralnet.md)
+- [感知机（Perceptron）、Adaline 与神经网络模型有何区别？](./faq/diff-perceptron-adaline-neuralnet.md)
- [What is the basic idea behind the dropout technique?](./faq/dropout.md)
+- [Dropout 技术的基本思路是什么？](./faq/dropout.md)
-##### Other Algorithms for Supervised Learning
+##### 其他监督学习算法
- [Why is Nearest Neighbor a Lazy Algorithm?](./faq/lazy-knn.md)
+- [为何最近邻（Nearest Neighbor）是惰性算法（Lazy Algorithm）？](./faq/lazy-knn.md)
-##### Unsupervised Learning
+##### 无监督学习（Unsupervised Learning）
- [What are some of the issues with clustering?](./faq/issues-with-clustering.md)
+- [聚类（clustering）有哪些常见问题？](./faq/issues-with-clustering.md)
-##### Semi-Supervised Learning
+##### 半监督学习（Semi-Supervised Learning）
- [What are the advantages of semi-supervised learning over supervised and unsupervised learning?](./faq/semi-vs-supervised.md)
+- [半监督学习相比监督学习与无监督学习有哪些优势？](./faq/semi-vs-supervised.md)
-##### Ensemble Methods
+##### 集成方法（Ensemble Methods）
- [Is Combining Classifiers with Stacking Better than Selecting the Best One?](./faq/logistic-boosting.md)
+- [用 Stacking 组合分类器是否优于只选最优的那一个？](./faq/logistic-boosting.md)
-##### Preprocessing, Feature Selection and Extraction
+##### 预处理、特征选择与特征提取
- [Why do we need to re-use training parameters to transform test data?](./faq/scale-training-test.md)
+- [为何需要用训练集拟合的参数来变换测试数据？](./faq/scale-training-test.md)
- [What are the different dimensionality reduction methods in machine learning?](./faq/dimensionality-reduction.md)
+- [机器学习中有哪些降维（dimensionality reduction）方法？](./faq/dimensionality-reduction.md)
- [What is the difference between LDA and PCA for dimensionality reduction?](./faq/lda-vs-pca.md)
+- [降维时 LDA 与 PCA 有何区别？](./faq/lda-vs-pca.md)
- [When should I apply data normalization/standardization?](./faq/when-to-standardize.md)
+- [何时应进行数据归一化/标准化（normalization/standardization）？](./faq/when-to-standardize.md)
- [Does mean centering or feature scaling affect a Principal Component Analysis?](./faq/pca-scaling.md)
+- [均值中心化或特征缩放会影响主成分分析（Principal Component Analysis）吗？](./faq/pca-scaling.md)
- [How do you attack a machine learning problem with a large number of features?](./faq/large-num-features.md)
+- [面对特征数量很多的机器学习问题，该如何着手？](./faq/large-num-features.md)
- [What are some common approaches for dealing with missing data?](./faq/missing-data.md)
+- [处理缺失数据有哪些常见做法？](./faq/missing-data.md)
- [What is the difference between filter, wrapper, and embedded methods for feature selection?](./faq/feature_sele_categories.md)
+- [特征选择的 filter、wrapper 与 embedded 方法有何区别？](./faq/feature_sele_categories.md)
- [Should data preparation/pre-processing step be considered one part of feature engineering? Why or why not?](./faq/dataprep-vs-dataengin.md)
+- [数据准备/预处理是否应算作特征工程的一部分？为什么？](./faq/dataprep-vs-dataengin.md)
- [Is a bag of words feature representation for text classification considered as a sparse matrix?](./faq/bag-of-words-sparsity.md)
+- [文本分类中的词袋（bag of words）特征表示是否可视为稀疏矩阵？](./faq/bag-of-words-sparsity.md)
-##### Naive Bayes
+##### 朴素贝叶斯（Naive Bayes）
- [Why is the Naive Bayes Classifier naive?](./faq/naive-naive-bayes.md)
+- [为何朴素贝叶斯分类器被称为 "naive"？](./faq/naive-naive-bayes.md)
- [What is the decision boundary for Naive Bayes?](./faq/naive-bayes-boundary.md)
+- [朴素贝叶斯的决策边界是什么？](./faq/naive-bayes-boundary.md)
- [Can I use Naive Bayes classifiers for mixed variable types?](./faq/naive-bayes-vartypes.md)
+- [朴素贝叶斯分类器能否用于混合变量类型？](./faq/naive-bayes-vartypes.md)
- [Is it possible to mix different variable types in Naive Bayes, for example, binary and continues features?](./naive-bayes-vartypes.md)
+- [朴素贝叶斯能否混合不同变量类型，例如二值特征与连续特征？](./naive-bayes-vartypes.md)
-##### Other
+##### 其他
- [What is Euclidean distance in terms of machine learning?](./faq/euclidean-distance.md)
+- [从机器学习角度看，欧氏距离（Euclidean distance）是什么？](./faq/euclidean-distance.md)
- [When should one use median, as opposed to the mean or average?](./faq/median-vs-mean.md)
+- [何时应使用中位数，而非均值或平均数？](./faq/median-vs-mean.md)
-##### Programming Languages and Libraries for Data Science and Machine Learning
+##### 数据科学与机器学习的编程语言与库
- [Is R used extensively today in data science?](./faq/r-in-datascience.md)
+- [R 语言如今是否仍广泛用于数据科学？](./faq/r-in-datascience.md)
- [What is the main difference between TensorFlow and scikit-learn?](./faq/tensorflow-vs-scikitlearn.md)
+- [TensorFlow 与 scikit-learn 的主要区别是什么？](./faq/tensorflow-vs-scikitlearn.md)
 <br>
@@ -418,23 +420,23 @@ starting to gather some of these great applications, and I'd be more than happy
-### Questions about the Book
+### 关于本书的问题
- [Can I use paragraphs and images from the book in presentations or my blog?](./faq/copyright.md)
+- [我能否在演示或博客中使用书中的段落与图片？](./faq/copyright.md)
- [How is this different from other machine learning books?](./faq/different.md)
+- [本书与其他机器学习书籍有何不同？](./faq/different.md)
- [Which version of Python was used in the code examples?](./faq/py2py3.md)
+- [代码示例使用的是哪个 Python 版本？](./faq/py2py3.md)
- [Which technologies and libraries are being used?](./faq/technologies.md)
+- [使用了哪些技术与库？](./faq/technologies.md)
- [Which book version/format would you recommend?](./faq/version.md)
+- [你推荐哪个版本/格式的书？](./faq/version.md)
- [Why did you choose Python for machine learning?](./faq/why-python.md)
+- [为何选择 Python 做机器学习？](./faq/why-python.md)
- [Why do you use so many leading and trailing underscores in the code examples?](./faq/underscore-convention.md)
+- [代码示例中为何使用这么多前导与尾随下划线？](./faq/underscore-convention.md)
- [What is the purpose of the `return self` idioms in your code examples?](./faq/return_self_idiom.md)
+- [代码示例中 `return self` 惯用法有何用途？](./faq/return_self_idiom.md)
- [Are there any prerequisites and recommended pre-readings?](./faq/prerequisites.md)
+- [有哪些先修要求与推荐阅读？](./faq/prerequisites.md)
- [How can I apply SVM to categorical data?](./faq/svm_for_categorical.md)
+- [如何将 SVM 用于分类数据？](./faq/svm_for_categorical.md)
-## Contact
+## 联系方式
-I am happy to answer questions! Just write me an [email](mailto:mail@sebastianraschka.com)
+我很乐意回答问题！请给我写[邮件](mailto:mail@sebastianraschka.com)
-or consider asking the question on the [Google Groups Email List](https://groups.google.com/forum/#!forum/python-machine-learning-book).
+，或在 [Google Groups 邮件列表](https://groups.google.com/forum/#!forum/python-machine-learning-book). 上提问。
-If you are interested in keeping in touch, I have quite a lively twitter stream ([@rasbt](https://twitter.com/rasbt)) all about data science and machine learning. I also maintain a [blog](http://sebastianraschka.com/articles.html) where I post all of the things I am particularly excited about.
+若想保持联系，我的 Twitter ([@rasbt](https://twitter.com/rasbt)) 上常有数据科学与机器学习相关动态。我还在[博客](http://sebastianraschka.com/articles.html) 分享我特别感兴趣的内容。