| 资源(55) | 时长( 11:29:11) | 播放量(108187) |
|---|---|---|
@1.1机器学习概述 |
00:14:47 | 2472 |
|
@1.2机器学习常用工具 |
00:17:50 | 2424 |
|
@1.3Scikit-learn概览 |
00:16:26 | 2386 |
|
@2.1数据预处理与预处理模块 |
00:08:32 | 2092 |
|
@2.2缺失值处理 |
00:16:08 | 1937 |
|
@2.3 离群值检测1 |
00:11:55 | 1912 |
|
@2.3 离群值检测2 |
00:15:52 | 1921 |
|
@2.4标准化 |
00:18:39 | 1897 |
|
@2.5 -1特征编码 |
00:07:33 | 1921 |
|
@2.5 -2特征离散化 |
00:16:09 | 1876 |
|
@3.1回归问题和Scikit-learn回归模块介绍 |
00:16:35 | 1852 |
|
@3.2线性回归 |
00:13:14 | 1875 |
|
@3.3-1线性回归问题 |
00:10:42 | 1869 |
|
@3.3-2偏差与方差 |
00:04:59 | 1863 |
|
@3.4-1稳健性回归 |
00:12:36 | 1858 |
|
@3.4-2其他回归方法 |
00:14:14 | 1838 |
|
@4.1-1 分类问题介绍 |
00:16:22 | 1826 |
|
@4.1-2 案例：根据多种特征预测学生的学业表现 |
00:04:23 | 1858 |
|
@4.2 逻辑回归 |
00:15:07 | 1842 |
|
@4.3 K近邻 |
00:16:10 | 1922 |
|
@4.4 决策树 |
00:19:43 | 1865 |
|
@4.5 朴素贝叶斯 |
00:18:04 | 1825 |
|
@4.6 支持向量机 |
00:14:43 | 1799 |
|
@5.1 聚类问题和cluster模块介绍 |
00:07:25 | 1801 |
|
@5.2 kmeans聚类 |
00:10:34 | 1808 |
|
@5.4 层次聚类 |
00:07:41 | 1805 |
|
@5.5 DBSCAN |
00:11:34 | 1806 |
|
@6.1-1 分类指标 |
00:19:51 | 1816 |
|
@6.1-2 分类指标 |
00:18:56 | 1784 |
|
@6.2 回归指标 |
00:06:08 | 1782 |
|
@6.3 聚类分析指标 |
00:11:16 | 1901 |
|
@6.4 交叉验证 |
00:16:42 | 1780 |
|
@6.5 参数调优 |
00:12:30 | 1804 |
|
@7.1-1 集成模型和ensemble模块介绍 |
00:06:59 | 1905 |
|
@7.1-2 Bagging |
00:13:39 | 1866 |
|
@7.2-1 AdaBoost |
00:12:02 | 1824 |
|
@7.2-2 GBDT |
00:08:32 | 1911 |
|
@7.3 VotingClassifier |
00:04:57 | 1890 |
|
@8.1 特征提取与特征选择概述 |
00:03:44 | 1902 |
|
@8.2-1 countvectorizer |
00:15:50 | 2092 |
|
@8.2-2 特征提取 |
00:05:49 | 2030 |
|
@8.3 过滤式 |
00:15:31 | 2032 |
|
@8.4 包裹式 |
00:05:44 | 2046 |
|
@8.5 嵌入式 |
00:03:20 | 2003 |
|
@9.3 非线性降维方法1 |
00:11:51 | 2114 |
|
@9.3 非线性降维方法2 |
00:09:16 | 2066 |
|
@9.3 非线性降维方法3 |
00:11:38 | 2040 |
|
@9.1 降维方法与sklearn降维模块概述 |
00:10:56 | 2041 |
|
@9.2 线性降维方法1 |
00:14:10 | 2018 |
|
@9.2 线性降维方法2 |
00:10:32 | 2010 |
|
@9.2 线性降维方法3 |
00:09:59 | 2087 |
|
计算思维之问题求解思想及方法1 |
00:38:28 | 2338 |
|
计算思维之问题求解思想及方法4 |
00:10:39 | 2309 |
|
计算思维之问题求解思想及方法3 |
00:09:24 | 2334 |
|
计算思维之问题求解思想及方法2 |
00:12:51 | 2312 |