update readme

README.md

@@ -132,31 +132,25 @@ Python Data Science Handbook [[book](https://jakevdp.github.io/PythonDataScience
 - [ ] [机器学习简介：分类、聚类等](https://jakevdp.github.io/PythonDataScienceHandbook/05.01-what-is-machine-learning.html) 
 - [ ] [scikit-learn 简单介绍](https://jakevdp.github.io/PythonDataScienceHandbook/05.02-introducing-scikit-learn.html) 
 - [ ] [scikit-learn KNN](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-intro.ipynb) 
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 - [ ] [scikit-learn 线性回归 demo1](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-linear-reg.ipynb) 
 - [ ] [scikit-learn 线性回归 demo2](https://github.com/YZHANG1270/Girls-In-AI/blob/master/machine_learning_diary/data_analysis/Linear_Regression.ipynb) 
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 - [ ] [scikit-learn 逻辑回归](https://github.com/YZHANG1270/Girls-In-AI/blob/master/machine_learning_diary/machine_learning/Logistic_Regression.ipynb) 
 - [ ] [scikit-learn Naive Bayes Classification](https://jakevdp.github.io/PythonDataScienceHandbook/05.05-naive-bayes.html) 
 - [ ] [scikit-learn 支持向量机 SVM](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-svm.ipynb) 
 - [ ] [scikit-learn 随机森林 Random Forest 1](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-random-forest.ipynb) 
 - [ ] [scikit-learn 随机森林 Random Forest 2](https://github.com/YZHANG1270/Girls-In-AI/blob/master/machine_learning_diary/machine_learning/Random_Forests.ipynb) 
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 - [ ] [scikit-learn K-Means](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-k-means.ipynb) 
 - [ ] [scikit-learn PCA](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-pca.ipynb) 
 - [ ] [scikit-learn 高斯混合模型](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-gmm.ipynb) 
 - [ ] [scikit-learn 模型验证和模型选择](https://nbviewer.jupyter.org/github/donnemartin/data-science-ipython-notebooks/blob/master/scikit-learn/scikit-learn-validation.ipynb) 
 - [ ] [实战项目：人脸检测](https://jakevdp.github.io/PythonDataScienceHandbook/05.14-image-features.html) 
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 - [ ] adaboost
 - [ ] xgboost
 - [ ] bagging, boosting
 - [ ] LightGBM
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 - [ ] LDA
 - [ ] 实战项目 - 影评分类
 - [ ] bonus：Auto ML- auto sklearn
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 - [ ] [超参数和模型验证](https://jakevdp.github.io/PythonDataScienceHandbook/05.03-hyperparameters-and-model-validation.html) 
 - [ ] 损失函数
 - [ ] 目标函数