初稿

AGNES.md

@@ -17,7 +17,7 @@ AGNES 算法是一种自底向上聚合的层次聚类算法，它先会将数
 
 最小距离描述的是两个簇之间距离最近的两个样本所对应的距离。例如下图中圆圈和菱形分别代表两个簇，两个簇之间离得最近的样本的**欧式距离**为 3.3 ，则最小距离为 3.3。
 
-![](./img/59.jpg)
+<div align=center><img src="../img/59.jpg"/></div>
 
 假设给定簇$$C_i$$与$$C_j$$，则最小距离为：$$d_{min}=min_{x\in i,z\in j}dist(x,z)$$
 
@@ -25,7 +25,7 @@ AGNES 算法是一种自底向上聚合的层次聚类算法，它先会将数
 
 最大距离描述的是两个簇之间距离最远的两个样本所对应的距离。例如下图中圆圈和菱形分别代表两个簇，两个簇之间离得最远的样本的**欧式距离**为 23.3 ，则最大距离为 23.3 。
 
-![](/img/60.jpg)
+<div align=center><img src="../img/60.jpg"/></div>
 
 假设给定簇$$C_i$$与$$C_j$$，则最大距离为：$$d_{min}=max_{x\in i,z\in j}dist(x,z)$$
 
@@ -33,7 +33,7 @@ AGNES 算法是一种自底向上聚合的层次聚类算法，它先会将数
 
 平均距离描述的是两个簇之间样本的平均距离。例如下图中圆圈和菱形分别代表两个簇，计算两个簇之间的所有样本之间的欧式距离并求其平均值。
 
-![](./img/61.jpg)
+<div align=center><img src="../img/61.jpg"/></div>
 
 假设给定簇$$C_i$$与$$C_j$$，$$|C_i|,|C_j|$$分别表示簇 i 与簇 j 中样本的数量，则平均距离为：$$d_{min}=\frac{1}{|C_i||C_j|}\sum_{x\in i}\sum_{z\in j}dist(x, z)$$
 
@@ -63,7 +63,7 @@ AGNES 算法是一种自底向上聚合的层次聚类算法，它先会将数
 
 如果将整个聚类过程中的合并，与合并的次序可视化出来，就能看出为什么说 AGNES 是自底向上的层次聚类算法了。
 
-![](./img/62.jpg)
+<div align=center><img src="../img/62.jpg"/></div>
 
 所以 AGNES 伪代码如下：
 

README.md

@@ -0,0 +1,4 @@
+本资料主要介绍一些机器学习的入门知识，例如什么是机器学习，常见的机器学习算法原理，常用的模型性能评估指标，怎样快速入门 sklearn 等内容。
+
+若想更加全面，系统的学习机器学习相关知识，可以在本书的最后扫码体验整套机器学习实训课程。该课程是与南京大学合作共建的实训课程，总共有 65 个实践任务，涵盖了《机器学习》中的前十章内容，并已在南京大学投入使用。
+

SUMMARY.md

@@ -15,4 +15,16 @@
   * [回归性能评估指标](regression_metrics.md)
   * [聚类性能评估指标](cluster_metrics.md)
 * [使用sklearn进行机器学习](sklearn.md)
+* [综合实战案例]()
+  * [泰坦尼克生还预测]()
+    * [简介](./titanic/introduction.md)
+    * [探索性数据分析(EDA)](./titanic/EDA.md)
+    * [特征工程](./titanic/feature engerning.md)
+    * [构建模型进行预测](./titanic/fit and predict.md)
+    * [调参](./titanic/tuning.md)
+  * [使用强化学习玩乒乓球游戏]()
+    * [什么是强化学习](./pingpong/what is reinforce learning.md)
+    * [Policy Gradient原理](./pingpong/Policy Gradient.md)
+    * [使用Policy Gradient玩乒乓球游戏](./pingpong/coding.md)
+* [实训推荐](recommand.md)
 

_book/algorithm.html

@@ -17,6 +17,10 @@
     
             
                 
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@@ -296,6 +300,180 @@
             
         </li>
     
+        <li class="chapter " data-level="1.6" >
+            
+                <span>
+            
+                    
+                    综合实战案例
+            
+                </span>
+            
+
+            
+            <ul class="articles">
+                
+    
+        <li class="chapter " data-level="1.6.1" >
+            
+                <span>
+            
+                    
+                    泰坦尼克生还预测
+            
+                </span>
+            
+
+            
+            <ul class="articles">
+                
+    
+        <li class="chapter " data-level="1.6.1.1" data-path="titanic/introduction.html">
+            
+                <a href="titanic/introduction.html">
+            
+                    
+                    简介
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="1.6.1.2" data-path="titanic/EDA.html">
+            
+                <a href="titanic/EDA.html">
+            
+                    
+                    探索性数据分析(EDA)
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="1.6.1.3" data-path="titanic/feature engerning.html">
+            
+                <a href="titanic/feature engerning.html">
+            
+                    
+                    特征工程
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="1.6.1.4" data-path="titanic/fit and predict.html">
+            
+                <a href="titanic/fit and predict.html">
+            
+                    
+                    构建模型进行预测
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="1.6.1.5" data-path="titanic/tuning.html">
+            
+                <a href="titanic/tuning.html">
+            
+                    
+                    调参
+            
+                </a>
+            
+
+            
+        </li>
+    
+
+            </ul>
+            
+        </li>
+    
+        <li class="chapter " data-level="1.6.2" >
+            
+                <span>
+            
+                    
+                    使用强化学习玩乒乓球游戏
+            
+                </span>
+            
+
+            
+            <ul class="articles">
+                
+    
+        <li class="chapter " data-level="1.6.2.1" data-path="pingpong/what is reinforce learning.html">
+            
+                <a href="pingpong/what is reinforce learning.html">
+            
+                    
+                    什么是强化学习
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="1.6.2.2" data-path="pingpong/Policy Gradient.html">
+            
+                <a href="pingpong/Policy Gradient.html">
+            
+                    
+                    Policy Gradient原理
+            
+                </a>
+            
+
+            
+        </li>
+    
+        <li class="chapter " data-level="1.6.2.3" data-path="pingpong/coding.html">
+            
+                <a href="pingpong/coding.html">
+            
+                    
+                    使用Policy Gradient玩乒乓球游戏
+            
+                </a>
+            
+
+            
+        </li>
+    
+
+            </ul>
+            
+        </li>
+    
+
+            </ul>
+            
+        </li>
+    
+        <li class="chapter " data-level="1.7" data-path="recommand.html">
+            
+                <a href="recommand.html">
+            
+                    
+                    实训推荐
+            
+                </a>
+            
+
+            
+        </li>
+    
 
     
 
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