梗直哥瞿炜-机器学习必修课：经典AI算法与编程实战
https://cdn.ldstatic.com/images/emoji/twemoji/file_folder.png?v=15 梗直哥瞿炜-机器学习必修课：经典AI算法与编程实战https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 06.2-3-研究哪些问题：分类、回归等.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 100.12-4-聚类算法代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 107.13-5-降维任务代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 113.14-3-EM算法参数估计.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 25.4-3-KNN分类任务代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 56.7-1-本章总览.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 111.14-1-本章总览.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 71.8-8-模型选择.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 24.4-2-KNN算法核心思想和原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 23.4-1本章总览.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 09.3-1本章总览：相互关系与学习路线.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 26.4-4-数据集划分：训练集与预测集.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 102.12-6-聚类算法优缺点和适用条件.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 63.7-8-决策树优缺点和适用条件.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 68.8-5-梯度下降优化算法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 14.3-6-Numpy基础：安装与性能对比.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 105.13-3-PCA求解算法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 96.11-8-集成学习优缺点和适用条件.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 58.7-3-信息熵.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 43.6-1-本章总览.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 83.10-1-本章总览.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 99.12-3-k-means和分层聚类.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 109.13-7-PCA在人脸识别中的应用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 81.9-9-SVM回归任务代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 52.6-10-LASSO和岭回归代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 89.11-1-本章总览.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 91.11-3-集成学习代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 05.2-2-数据长什么样：常见数据集、典型实例、如何使用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 12.3-4-JupyterNotebook基础使用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 07.2-4-如何分门别类：监督、无监督、强化学习等.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 106.13-4-PCA算法代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 069- 机器学习必修课：经典AI算法与编程实战
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 112.14-2-概率图模型核心思想和原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 34.5-3-逻辑回归核心思想和原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 40.5-9多分类策略.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 116.15-1-本章总览.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 108.13-6-PCA在数据降噪中的应用.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 51.6-9-正则化.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 65.8-2-神经网络核心思想和原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 60.7-5-基尼系数.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 47.6-5-过拟合与欠拟合.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 50.6-8-模型误差.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 44.6-2-损失函数.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 31.4-9-KNN优缺点和适用条件.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 84.10-2-贝叶斯方法核心思想和原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 110.13-8-主成分分析优缺点和适用条件.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 21.3-13-Numpy数组神奇索引和布尔索引.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 03.1-3-课程使用的技术栈.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 97.12-1-本章总览.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 49.6-7-交叉验证.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 04.2-1本章总览.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 77.9-5-线性SVM分类任务代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 30.4-8-KNN回归任务代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 17.3-9-Numpy非常重要的数组合并与拆分操作.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 80.9-8-非线性SVM代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 78.9-6-非线性SVM：核技巧.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 115.14-5-概率图模型优缺点和适用条件.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 118.15-3-房价预测.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 72.8-9-神经网络优缺点和适用条件.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 70.8-7-梯度消失和梯度爆炸.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 22.3-14-Matplotlib数据可视化：基础绘制与设置.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 76.9-4-SVM软间隔.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 29.4-7-特征归一化.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 46.6-4-决策边界.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 88.10-6-贝叶斯方法优缺点和适用条件.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 94.11-6-串行策略：Boosting.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 61.7-6-决策树剪枝.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 01.1-1课程内容和理念.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 92.11-4-并行策略：Bagging、OOB等方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 98.12-2-聚类算法核心思想和原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 75.9-3-硬间隔SVM.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 86.10-4-朴素贝叶斯的代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 02.1-2-初识机器学习.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 79.9-7-SVM核函数.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 45.6-3-梯度下降.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 38.5-7-逻辑回归算法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 87.10-5-多项式朴素贝叶斯代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 114.14-4-隐马尔可夫模型代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 10.3-2-Anaconda图形化操作.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 103.13-1-本章总览.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 85.10-3-朴素贝叶斯分类.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 93.11-5-并行策略：随机森林.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 42.5-11-线性算法优缺点和适用条件.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 36.5-5-模型评价：MSE、RMSE、MAE和R方.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 90.11-2-集成学习核心思想和原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 55.6-13-评价指标：ROC曲线.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 32.5-1-本章总览.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 11.3-3-Anaconda命令行操作.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 16.3-8-Numpy数组基础索引：索引和切片.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 82.9-10-SVM优缺点和适用条件.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 48.6-6-学习曲线.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 08.2-5-机器学习的七大常见误区和局限.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 28.4-6-超参数.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 95.11-7-结合策略：Stacking方法.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 117.15-2-泰坦尼克生还预测.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 39.5-8-线性逻辑回归代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 57.7-2-决策树核心思想和原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 104.13-2-PCA核心思想和原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 73.9-1-本章总览.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 74.9-2-SVM核心思想和原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 35.5-4-线性回归代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 101.12-5-聚类评估代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 41.5-10-复杂逻辑回归及代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 53.6-11-模型泛化.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 69.8-6-神经网络简单代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 19.3-11-Numpy数组统计运算：常用的都在这儿了.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 33.5-2-线性回归核心思想和原理.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 62.7-7-决策树回归任务代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 37.5-6多项式回归代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 54.6-12-评价指标：混淆矩阵、精准率和召回率.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 119.15-4-交易反欺诈代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 64.8-1-本章总览.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 59.7-4-决策树分类任务代码实现.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 67.8-4-正向传播与反向传播.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 18.3-10-Numpy数组矩阵运算：一元运算、二元运算与矩阵运算.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 27.4-5-模型评价.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 66.8-3-激活函数.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 13.3-5-JupyterNotebook高级使用：常用魔法命令.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 20.3-12-Numpy数组arg运算和排序.mp4
https://cdn.ldstatic.com/images/emoji/twemoji/page_facing_up.png?v=15 15.3-7-Numpy数组创建：特定数组、等差数组、随机数组.mp4
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