2016年邹博-《机器学习》升级版Ⅱ-小象学院
视频+pdf课件 共24次课，每次两小时
课程介绍：
课程风格通俗易懂，真实案例实战。精心挑选真实的数据集为案例，通过python数据科学库numpy,pandas,matplot结合机器学习库scikit-learn完成一些列的机器学习案例。课程以实战为基础，所有课时都结合代码演示如何使用这些python库来完成一个真实的数据案例。算法与项目相结合，选择经典kaggle项目，从数据预处理开始一步步代码实战带大家入门机器学习。
学习收益：
1.掌握Python数据科学工具包，包括矩阵数据处理与可视化展示。
2.掌握机器学习算法原理推导，从数学上理解算法是怎么来的以及其中涉及的细节。
3.掌握每一个算法所涉及的参数，详解其中每一步对结果的影响。
4.熟练使用Python进行建模实战，基于真实数据集展开分析，一步步完成整个建模实战任务。
课程目录：
第1章：机器学习应用实验手册
机器学习应用实验手册
第2章：第一周课件和录播视频
1.数学分析与概率论
1.数学分析与概率论（视频）
2.数理统计与参数估计
2.数理统计与参数估计（视频）
第3章：第二周课件和录播视频
3.矩阵和线性代数
3.矩阵和线性代数（视频）
4.凸优化
凸优化(视频)
5.Python库
5.Python库（视频）
第4章：第三周课件和录播视频
6.Python库II
6.Python库II （视频）
7.回归
7.回归 （视频）
8.回归实践
8.回归实践（视频）
第5章：第四周课件和录播视频
9.决策树和随机森林
9.决策树和随机森林（视频）
10.决策树和随机森林实践
10.决策树和随机森林实践（视频）
11.提升
提升(视频)
第6章：第五周课件和录播视频
12.XGBoost实践
12.XGBoost实践（视频）
13.SVM
13.SVM（视频）
14.SVM实践
14.SVM实践（视频）
第7章：第六周课件和录播视频
15.聚类
15. 聚类（视频）
16.聚类实践
16. 聚类实践（视频）
17.EM算法
17. EM算法（视频）
第8章：第七周课件和录播视频
18.EM算法实践
18. EM算法实践（视频）
19.贝叶斯网络
19. 贝叶斯网络（视频）
20.朴素贝叶斯实践
20.朴素贝叶斯实践（视频）
第9章：第八周课件和录播视频
21.主题模型
视频-21.主题模型
22.主题模型实践
22.主题模型实践（视频）
23.HMM
23. HMM（视频）
24.HMM实践
24. HMM实践（视频）
视频截图：
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