文件内容:
file:12-6-12-6聚类算法优缺点和适用条件.mp4
file:11-4-11-4并行策略：Bagging、OOB等方法.mp4
file:05-2-5-2线性回归核心思想和原理.mp4
file:06-5-6-5过拟合与欠拟合.mp4
file:09-8-9-8非线性SVM代码实现.mp4
file:03-10-3-10Numpy数组矩阵运算：一元运算、二元运算与矩阵运算.mp4
file:06-4-6-4决策边界.mp4
file:10-1-10-1本章总览.mp4
file:15-3-15-3房价预测.mp4
file:10-3-10-3朴素贝叶斯分类.mp4
file:04-6-4-6超参数.mp4
file:07-4-7-4决策树分类任务代码实现.mp4
file:05-9-5-9多分类策略.mp4
file:11-6-11-6串行策略：Boosting.mp4
file:06-6-6-6学习曲线.mp4
file:11-8-11-8集成学习优缺点和适用条件.mp4
file:12-5-12-5聚类评估代码实现.mp4
file:15-2-15-2泰坦尼克生还预测.mp4
file:09-5-9-5线性SVM分类任务代码实现.mp4
file:04-7-4-7特征归一化.mp4
file:05-11-5-11线性算法优缺点和适用条件.mp4
file:04-2-4-2KNN算法核心思想和原理.mp4
file:03-4-3-4JupyterNotebook基础使用.mp4
file:11-7-11-7结合策略：Stacking方法.mp4
file:03-1-3-1本章总览：相互关系与学习路线.mp4
file:09-9-9-9SVM回归任务代码实现.mp4
file:04-5-4-5模型评价.mp4
file:04-8-4-8KNN回归任务代码实现.mp4
file:02-4-2-4如何分门别类：监督、无监督、强化学习等.mp4
file:03-14-3-14Matplotlib数据可视化：基础绘制与设置.mp4
folder:011234_机器学习必修经典算法与Python实战