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# 机器学习之路 - [Scikit-learn 中文手册](#id1) - [构建虚拟环境](#id2) - [Python 学习](#id3) - [牛人 blog](#id4) - [XGBOOST 安装使用](#id5) - [Matplotlib 处理乱码](#id6) - [git 使用教程](#id7)

Scikit-learn 中文手册

http://sklearn.lzjqsdd.com/ 1.scikit-learn 安装 http://scikit-learn.org/dev/install.html ``` pip install -U scikit-learn ``` 安装 scikit-learn 之前需要安装 numpy [在Ubuntu 14.04 64bit上安装numpy和matplotlib库](1) ``` sudo apt-get install python-numpy sudo apt-get install python-scipy sudo apt-get install python-matplotlib ``` 2.scikit-learn 学习笔记 http://www.cnblogs.com/maybe2030/p/4583087.html 3.在Python中实现你自己的推荐系统 http://python.jobbole.com/85516/ pydotplus.graphviz.InvocationException: GraphViz's executables not found 4.重新安装 graphviz http://www.graphviz.org/Download_linux_ubuntu.php 5.决策树案例 http://scikit-learn.org/stable/modules/tree.html#classification 6.安装 python-tk,与 matplotlib.pyplot 相关联 sudo apt-get install python-tk **交叉校验,逻辑回归,线性回归**

构建虚拟环境

``` # 首先安装 virtualenv sudo pip install virtualenv # 再用 virtualenv 创建版本 virtualenv ~/env ``` [Python 虚拟环境：Virtualenv](http://www.liaoxuefeng.com/wiki/0014316089557264a6b348958f449949df42a6d3a2e542c000/001432712108300322c61f256c74803b43bfd65c6f8d0d0000) 利用 Pycharm 可以继承本地的环境 ![enter description here][1] **启动虚拟环境** ``` cd ~/env2.7 source ./bin/activate ``` **退出虚拟环境** ``` deactivate ``` [使用 virtualenvwrapper](http://blog.csdn.net/luckytanggu/article/details/51592091) 创建不同的虚拟环境，并进行管理;在 pycharm 中通过路经指定具体的python版本解释器 ``` pip install virtualenvwrapper ``` **虚拟环境中运行 ipython** 先在虚拟环境中安装 ipython ``` pip install ipython ``` 再对 ipython 启动进行设置 [Running iPython cleanly inside a virtualenv](4) ``` alias ipy="python -c 'import IPython; IPython.terminal.ipapp.launch_new_instance()'" ipy notebook # 启动 ipython notebook ```

Python 学习

[Python 学习之](3)

牛人 blog

[Bryan__的专栏](http://blog.csdn.net/bryan__) [止战。机器学习](http://www.cnblogs.com/zhizhan/tag/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/)

XGBOOST 安装使用

[官网](https://xgboost.readthedocs.io/en/latest/build.html) [XGBOOST installation](https://github.com/dmlc/xgboost/blob/master/doc/python/python_intro.md) ``` git clone --recursive https://github.com/dmlc/xgboost cd xgboost; make -j4 ``` To install XGBoost, do the following: - Run make in the root directory of the project - In the python-package directory, run ``` python setup.py install ```

matplotlib 中文乱码

[处理乱码](sample/knn/)

[git 使用教程](https://git-scm.com/book/zh/v2/%E8%B5%B7%E6%AD%A5-%E5%88%9D%E6%AC%A1%E8%BF%90%E8%A1%8C-Git-%E5%89%8D%E7%9A%84%E9%85%8D%E7%BD%AE)

[1]: ./images/1479741888781.jpg "1479741888781.jpg" [2]: http://liuzhijun.iteye.com/blog/1872241 [3]: ./python [4]: https://coderwall.com/p/xdox9a/running-ipython-cleanly-inside-a-virtualenv

Name		Name	Last commit message	Last commit date
Latest commit History 169 Commits
DataCastle		DataCastle
PIC		PIC
Spark		Spark
blog		blog
images		images
kaggle		kaggle
ml-100k		ml-100k
ml_practice		ml_practice
pandas_test		pandas_test
pcredit/Credit		pcredit/Credit
predict_risk		predict_risk
python		python
sample		sample
README.md		README.md
ToDoList.md		ToDoList.md
decisionTree.py		decisionTree.py
iris.pdf		iris.pdf
plot_cv_predict.py		plot_cv_predict.py
recommend.py		recommend.py
regision_linear_pizza.py		regision_linear_pizza.py
sample_output.txt		sample_output.txt
shell_python.py		shell_python.py
协同过滤.md		协同过滤.md
机器学习.md		机器学习.md

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Scikit-learn 中文手册

构建虚拟环境

Python 学习

牛人 blog

XGBOOST 安装使用

matplotlib 中文乱码

[git 使用教程](https://git-scm.com/book/zh/v2/%E8%B5%B7%E6%AD%A5-%E5%88%9D%E6%AC%A1%E8%BF%90%E8%A1%8C-Git-%E5%89%8D%E7%9A%84%E9%85%8D%E7%BD%AE)

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