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# 机器学习
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* [简介](README.md)
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* [第1章：机器学习概述](machine_learning.md)
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* [1.1 什么是机器学习](what's machine_learning.md)
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* [1.2 机器学习常见术语](some term.md)
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* [1.3 机器学习项目流程](work flow.md)
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* [第2章：常见机器学习算法](algorithm.md)
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* [2.1 kNN](kNN.md)
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* [2.2 线性回归](linear_regression.md)
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* [2.3 逻辑回归](logistic_regression.md)
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* [2.4 多分类学习](multi-class-learning.md)
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* [2.5 决策树](decision_tree.md)
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* [2.6 随机森林](random_forest.md)
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* [2.7 朴素贝叶斯分类器](bayes.md)
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* [2.8 支持向量机](svm.md)
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* [2.9 kMeans](kMeans.md)
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* [2.10 AGNES](AGNES.md)
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* [第3章：模型评估指标](metrics.md)
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* [3.1 常用分类性能评估指标](classification_metrics.md)
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* [3.2 常用回归性能评估指标](regression_metrics.md)
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* [3.3 常用聚类性能评估指标](cluster_metrics.md)
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* [第4章：使用sklearn进行机器学习](sklearn.md)
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* [第5章：综合实战案例](xx introduction.md)
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* [5.1 泰坦尼克生还预测](./titanic/introduction.md)
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* [5.1.1 探索性数据分析(EDA)](./titanic/EDA.md)
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* [5.1.2 特征工程](./titanic/feature engerning.md)
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* [5.1.3 构建模型进行预测](./titanic/fit and predict.md)
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* [5.1.4 调参](./titanic/tuning.md)
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* [5.2 使用强化学习玩乒乓球游戏](./pingpong/introduction.md)
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* [5.2.1 什么是强化学习](./pingpong/what is reinforce learning.md)
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* [5.2.2 Policy Gradient原理](./pingpong/Policy Gradient.md)
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* [5.2.3 使用Policy Gradient玩乒乓球游戏](./pingpong/coding.md)
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* [实训推荐](recommand.md)
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