文件内容:
file:07-2决策树核心思想和原理.mp4
file:11-8集成学习优缺点和适用条件.mp4
file:03-11Numpy数组统计运算：常用的都在这儿了.mp4
file:03-1本章总览：相互关系与学习路线.mp4
file:03-8Numpy数组基础索引：索引和切片.mp4
file:06-6学习曲线.mp4
file:10-3朴素贝叶斯分类.mp4
file:01-2初识机器学习.mp4
file:11-7结合策略：Stacking方法.mp4
file:06-5过拟合与欠拟合.mp4
file:06-3梯度下降.mp4
file:05-5模型评价：MSE、RMSE、MAE和R方.mp4
file:06-10LASSO和岭回归代码实现.mp4
file:05-8线性逻辑回归代码实现.mp4
file:06-8模型误差.mp4
file:07-1本章总览.mp4
file:06-4决策边界.mp4
file:06-1本章总览.mp4
file:01-3课程使用的技术栈.mp4
file:05-9多分类策略.mp4
file:02-2数据长什么样：常见数据集、典型实例、如何使用.mp4
file:03-12Numpy数组arg运算和排序.mp4
file:05-11线性算法优缺点和适用条件.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:05-2线性回归核心思想和原理.mp4
file:09-1本章总览.mp4
file:09-5线性SVM分类任务代码实现.mp4
file:08-4正向传播与反向传播.mp4
file:08-6神经网络简单代码实现.mp4
file:06-9正则化.mp4
folder:B站 - 机器学习必修课：经典AI算法与编程实战 瞿炜