B站 - 机器学习必修课:经典AI算法与编程实战 瞿炜
- file:更多.pdf
- file:image(2).png
- file:02-5机器学习的七大常见误区和局限.mp4
- file:03-13Numpy数组神奇索引和布尔索引.mp4
- file:03-5JupyterNotebook高级使用:常用魔法命令.mp4
- file:03-10Numpy数组矩阵运算:一元运算、二元运算与矩阵运算.mp4
- file:03-2Anaconda图形化操作.mp4
- file:02-4如何分门别类:监督、无监督、强化学习等.mp4
- file:01-3课程使用的技术栈.mp4
- file:03-1本章总览:相互关系与学习路线.mp4
- file:03-6Numpy基础:安装与性能对比.mp4
- file:07-3信息熵.mp4
- file:07-7决策树回归任务代码实现.mp4
- file:07-8决策树优缺点和适用条件.mp4
- file:06-3梯度下降.mp4
- file:09-3硬间隔SVM.mp4
- file:11-8集成学习优缺点和适用条件.mp4
- file:09-4SVM软间隔.mp4
- file:06-9正则化.mp4
- file:07-6决策树剪枝.mp4
- file:07-5基尼系数.mp4
- file:01-1课程内容和理念.mp4
- file:09-5线性SVM分类任务代码实现.mp4
- file:06-12评价指标:混淆矩阵、精准率和召回率.mp4
- file:15-5如何深入研究机器学习.mp4
- file:13-3PCA求解算法.mp4
- file:09-10SVM优缺点和适用条件.mp4
- file:10-1本章总览.mp4
- file:10-6贝叶斯方法优缺点和适用条件.mp4
- folder:B站 - 机器学习必修课:经典AI算法与编程实战 瞿炜
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