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零基���人工�����机器学习(f��n) 视频教程 下蝲
相关截图�Q?/strong>
![]() 资料目录�Q?/strong>
├─01-�W�一模块�Q�Python快速入�?/div>
�?nbsp; �?/div>
�?nbsp; ├─01-1-Python环境配置
�?nbsp; �?nbsp; 01-1-Python环境配置.ts
�?nbsp; �?/div>
�?nbsp; ├─02-2-Python库安装工�?/div>
�?nbsp; �?nbsp; 01-2-Python库安装工�?ts
�?nbsp; �?/div>
�?nbsp; ├─03-3-Notebook工具使用
�?nbsp; �?nbsp; 01-3-Notebook工具使用.ts
�?nbsp; �?/div>
�?nbsp; ├─04-4-Python����?/div>
�?nbsp; �?nbsp; 01-4-Python����?ts
�?nbsp; �?/div>
�?nbsp; ├─05-5-Python数��D����?/div>
�?nbsp; �?nbsp; 01-5-Python数��D����?ts
�?nbsp; �?/div>
�?nbsp; ├─06-6-Python字符串操�?/div>
�?nbsp; �?nbsp; 01-6-Python字符串操�?ts
�?nbsp; �?/div>
�?nbsp; ├─07-7-1-索引�l�构
�?nbsp; �?nbsp; 01-7-1-索引�l�构.ts
�?nbsp; �?/div>
�?nbsp; ├─08-7-2-List基础�l�构
�?nbsp; �?nbsp; 01-7-2-List基础�l�构.ts
�?nbsp; �?/div>
�?nbsp; ├─09-8-List核心操作
�?nbsp; �?nbsp; 01-8-List核心操作.ts
�?nbsp; �?/div>
�?nbsp; ├─10-9-字典基础定义
�?nbsp; �?nbsp; 01-9-字典基础定义.ts
�?nbsp; �?/div>
�?nbsp; ├─11-10-字典的核心操�?/div>
�?nbsp; �?nbsp; 01-10-字典的核心操�?ts
�?nbsp; �?/div>
�?nbsp; ├─12-11-Set�l�构
�?nbsp; �?nbsp; 01-11-Set�l�构.ts
�?nbsp; �?/div>
�?nbsp; ├─13-12-赋值机�?/div>
�?nbsp; �?nbsp; 01-12-赋值机�?ts
�?nbsp; �?/div>
�?nbsp; ├─14-13-判断�l�构
�?nbsp; �?nbsp; 01-13-判断�l�构.ts
�?nbsp; �?/div>
�?nbsp; ├─15-14-循环�l�构
�?nbsp; �?nbsp; 01-14-循环�l�构.ts
�?nbsp; �?/div>
�?nbsp; ├─16-15-函数定义
�?nbsp; �?nbsp; 01-15-函数定义.ts
�?nbsp; �?/div>
�?nbsp; ├─17-16-模块与包
�?nbsp; �?nbsp; 01-16-模块与包.ts
�?nbsp; �?/div>
�?nbsp; ├─18-17-异常处理模块
�?nbsp; �?nbsp; 01-17-异常处理模块.ts
�?nbsp; �?/div>
�?nbsp; ├─19-18-文�g操作
�?nbsp; �?nbsp; 01-18-文�g操作.ts
�?nbsp; �?/div>
�?nbsp; ├─20-19-�cȝ��基本定义
�?nbsp; �?nbsp; 01-19-�cȝ��基本定义.ts
�?nbsp; �?/div>
�?nbsp; ├─21-20-�cȝ��属性操�?/div>
�?nbsp; �?nbsp; 01-20-�cȝ��属性操�?ts
�?nbsp; �?/div>
�?nbsp; ├─22-21-旉���操作
�?nbsp; �?nbsp; 01-21-旉���操作.ts
�?nbsp; �?/div>
�?nbsp; ├─23-22-Python�l�习(f��n)�?1
�?nbsp; �?nbsp; 01-22-Python�l�习(f��n)�?1.ts
�?nbsp; �?/div>
�?nbsp; └─24-23-Python�l�习(f��n)�?2
�?nbsp; 01-23-Python�l�习(f��n)�?2.ts
�?/div>
├─02-�W�二模块�Q�Python数据�U�学必备工具包实�?/div>
�?nbsp; ├─01-�U�学计算�?Numpy
�?nbsp; �?nbsp; 01-1-Numpy概述.ts
�?nbsp; �?nbsp; 02-2-Array数组.ts
�?nbsp; �?nbsp; 03-3-数组�l�构.ts
�?nbsp; �?nbsp; 04-4-数组�c�d��.ts
�?nbsp; �?nbsp; 05-5-数��D����?ts
�?nbsp; �?nbsp; 06-6-排序操作.ts
�?nbsp; �?nbsp; 07-7-数组形状操作.ts
�?nbsp; �?nbsp; 08-8-数组生成函数.ts
�?nbsp; �?nbsp; 09-9-常用生成函数.ts
�?nbsp; �?nbsp; 10-10-四则�q�算.ts
�?nbsp; �?nbsp; 11-11-随机模块.ts
�?nbsp; �?nbsp; 12-12-文�g��d��.ts
�?nbsp; �?nbsp; 13-13-数组保存.ts
�?nbsp; �?nbsp; 14-14-�l�习(f��n)�?1.ts
�?nbsp; �?nbsp; 15-15-�l�习(f��n)�?2.ts
�?nbsp; �?nbsp; 16-16-�l�习(f��n)�?3.ts
�?nbsp; �?nbsp; 17-13-Pandas常用操作.ts
�?nbsp; �?nbsp; 18-14-Pandas常用操作2.ts
�?nbsp; �?/div>
�?nbsp; ├─02-数据分析处理�?Pandas
�?nbsp; �?nbsp; 01-1-Pandas概述.ts
�?nbsp; �?nbsp; 02-2-Pandas基本操作.ts
�?nbsp; �?nbsp; 03-3-Pandas索引.ts
�?nbsp; �?nbsp; 04-4-groupby操作.ts
�?nbsp; �?nbsp; 05-5-数��D����?.ts
�?nbsp; �?nbsp; 06-6-对象操作.ts
�?nbsp; �?nbsp; 07-7-对象操作2.ts
�?nbsp; �?nbsp; 08-8-merge操作.ts
�?nbsp; �?nbsp; 09-9-昄���讄���.ts
�?nbsp; �?nbsp; 10-10-数据透视�?ts
�?nbsp; �?nbsp; 11-11-旉���操作.ts
�?nbsp; �?nbsp; 12-12-旉���序列操作.ts
�?nbsp; �?nbsp; 13-15-Groupby操作延��.ts
�?nbsp; �?nbsp; 14-16-字符串操�?ts
�?nbsp; �?nbsp; 15-17-索引�q�阶.ts
�?nbsp; �?nbsp; 16-18-Pandas�l�图操作.ts
�?nbsp; �?nbsp; 17-19-大数据处理技�?ts
�?nbsp; �?/div>
�?nbsp; ├─03-.可视化库-Matplotlib
�?nbsp; �?nbsp; 01-1-Matplotlib概述.ts
�?nbsp; �?nbsp; 02-2-子图与标�?ts
�?nbsp; �?nbsp; 03-3-风格讄���.ts
�?nbsp; �?nbsp; 04-4-条�Ş�?ts
�?nbsp; �?nbsp; 05-5-条�Ş囄����?ts
�?nbsp; �?nbsp; 06-6-条�Ş囑֤��?ts
�?nbsp; �?nbsp; 07-7-盒图�l�制.ts
�?nbsp; �?nbsp; 08-8-盒图�l�节.ts
�?nbsp; �?nbsp; 09-9-�l�图�l�节讄���.ts
�?nbsp; �?nbsp; 10-10-�l�图�l�节讄���2.ts
�?nbsp; �?nbsp; 11-11-直方图与散点�?ts
�?nbsp; �?nbsp; 12-12-3D囄����?ts
�?nbsp; �?nbsp; 13-13-pie�?ts
�?nbsp; �?nbsp; 14-14-子图布局.ts
�?nbsp; �?nbsp; 15-15-�l�合pandas与sklearn.ts
�?nbsp; �?/div>
�?nbsp; └─04-可视化库-Seaborn
�?nbsp; 01-0-评�������?ts
�?nbsp; 02-1整体布局风格讄���.ts
�?nbsp; 03-2风格�l�节讄���.ts
�?nbsp; 04-3调色�?ts
�?nbsp; 05-4调色杉K��色设�|?ts
�?nbsp; 06-5单变量分析绘�?ts
�?nbsp; 07-6回归分析�l�图.ts
�?nbsp; 08-7多变量分析绘�?ts
�?nbsp; 09-8分类属性绘�?ts
�?nbsp; 10-9Facetgrid使用�Ҏ(gu��)��.ts
�?nbsp; 11-10Facetgrid�l�制多变�?ts
�?nbsp; 12-11热度囄����?ts
�?/div>
├─03-�W�三模块�Q��h工智�?必备数学评���
�?nbsp; ├─01-高等数学基础
�?nbsp; �?nbsp; 01-0-评�������?ts
�?nbsp; �?nbsp; 02-1-函数.ts
�?nbsp; �?nbsp; 03-2-极限.ts
�?nbsp; �?nbsp; 04-3-无穷���与无穷�?ts
�?nbsp; �?nbsp; 05-4-�q�箋性与导数.ts
�?nbsp; �?nbsp; 06-5-偏导�?ts
�?nbsp; �?nbsp; 07-6-方向导数.ts
�?nbsp; �?nbsp; 08-7-梯度.ts
�?nbsp; �?/div>
�?nbsp; ├─02-微积�?/div>
�?nbsp; �?nbsp; 01-1-微积分基本想�?ts
�?nbsp; �?nbsp; 02-2-微积分的解释.ts
�?nbsp; �?nbsp; 03-3-定积�?ts
�?nbsp; �?nbsp; 04-4-定积分性质.ts
�?nbsp; �?nbsp; 05-5-牛顿-莱布?y��u)��D��公式.ts
�?nbsp; �?/div>
�?nbsp; ├─03-泰勒公式与拉格朗�?/div>
�?nbsp; �?nbsp; 01-1-泰勒公式出发�?ts
�?nbsp; �?nbsp; 02-2-一点一世界.ts
�?nbsp; �?nbsp; 03-3-阶数的作�?ts
�?nbsp; �?nbsp; 04-4-阶乘的作�?ts
�?nbsp; �?nbsp; 05-5-拉格朗日乘子�?ts
�?nbsp; �?nbsp; 06-6-求解拉格朗日乘子�?ts
�?nbsp; �?/div>
�?nbsp; ├─04-�U�性代数基���
�?nbsp; �?nbsp; 01-1-行列式概�q?ts
�?nbsp; �?nbsp; 02-2-矩阵与数据的关系.ts
�?nbsp; �?nbsp; 03-3-矩阵基本操作.ts
�?nbsp; �?nbsp; 04-4-矩阵的几�U�变�?ts
�?nbsp; �?nbsp; 05-5-矩阵的秩.ts
�?nbsp; �?nbsp; 06-6-内积与正�?ts
�?nbsp; �?/div>
�?nbsp; ├─05-特征��g��矩阵分解
�?nbsp; �?nbsp; 01-1-特征��g��特征向量.ts
�?nbsp; �?nbsp; 02-2-特征�I�间与应�?ts
�?nbsp; �?nbsp; 03-1-SVD要解决的问题.ts
�?nbsp; �?nbsp; 04-4-特征值分�?ts
�?nbsp; �?nbsp; 05-5-SVD矩阵分解.ts
�?nbsp; �?/div>
�?nbsp; ├─06-随机变量
�?nbsp; �?nbsp; 01-1-���L��型随机变�?ts
�?nbsp; �?nbsp; 02-2-�q�箋型随机变�?ts
�?nbsp; �?nbsp; 03-3-���单随机抽�?ts
�?nbsp; �?nbsp; 04-4-似然函数.ts
�?nbsp; �?nbsp; 05-5-极大似然估计.ts
�?nbsp; �?/div>
�?nbsp; ├─07-概率论基���
�?nbsp; �?nbsp; 01-1-概率与频�?ts
�?nbsp; �?nbsp; 02-2-古典概型.ts
�?nbsp; �?nbsp; 03-3-条�g概率.ts
�?nbsp; �?nbsp; 04-4-条�g概率���例�?ts
�?nbsp; �?nbsp; 05-5-独立�?ts
�?nbsp; �?nbsp; 06-6-二维���L��型随机变�?ts
�?nbsp; �?nbsp; 07-7-二维�q�箋型随机变�?ts
�?nbsp; �?nbsp; 08-8-边缘分布.ts
�?nbsp; �?nbsp; 09-9-期望.ts
�?nbsp; �?nbsp; 10-10-期望求解.ts
�?nbsp; �?nbsp; 11-11-马尔�U�夫不等�?ts
�?nbsp; �?nbsp; 12-12-切比雪夫不等�?ts
�?nbsp; �?nbsp; 13-13-后验概率估计.ts
�?nbsp; �?nbsp; 14-14-贝叶斯拼写纠错实�?ts
�?nbsp; �?nbsp; 15-15-垃圾邮�g�q���o(h��)实例.ts
�?nbsp; �?/div>
�?nbsp; ├─08-数据�U�学你得知道的几�U�分�?/div>
�?nbsp; �?nbsp; 01-1-正太分布.ts
�?nbsp; �?nbsp; 02-2-二项式分�?ts
�?nbsp; �?nbsp; 03-3-泊松分布.ts
�?nbsp; �?nbsp; 04-4-均匀分布.ts
�?nbsp; �?nbsp; 05-5-卡方分布.ts
�?nbsp; �?nbsp; 06-6-beta分布.ts
�?nbsp; �?/div>
�?nbsp; ├─09-核函数变�?/div>
�?nbsp; �?nbsp; 01-1-核函数的目的.ts
�?nbsp; �?nbsp; 02-2-�U�性核函数.ts
�?nbsp; �?nbsp; 03-3-多项式核函数.ts
�?nbsp; �?nbsp; 04-4-核函数实�?ts
�?nbsp; �?nbsp; 05-5-高斯核函�?ts
�?nbsp; �?nbsp; 06-6-参数的媄(ji��ng)�?ts
�?nbsp; �?/div>
�?nbsp; ├─10-熵与�Ȁ�z�d���?/div>
�?nbsp; �?nbsp; 01-1-�늚�概念.ts
�?nbsp; �?nbsp; 02-2-�늚�大小意味着什�?ts
�?nbsp; �?nbsp; 03-3-�Ȁ�z�d���?ts
�?nbsp; �?nbsp; 04-4-�Ȁ�z�d��数的问题.ts
�?nbsp; �?/div>
�?nbsp; ├─11-回归分析
�?nbsp; �?nbsp; 01-1-回归分析概述.ts
�?nbsp; �?nbsp; 02-2-回归方程定义.ts
�?nbsp; �?nbsp; 03-3-误差��的定义.ts
�?nbsp; �?nbsp; 04-4-最���二乘法推导与求�?ts
�?nbsp; �?nbsp; 05-5-回归方程求解���例�?ts
�?nbsp; �?nbsp; 06-6-回归直线拟合优度.ts
�?nbsp; �?nbsp; 07-7-多元与曲�U�回归问�?ts
�?nbsp; �?nbsp; 08-8-Python工具包介�l?ts
�?nbsp; �?nbsp; 09-9-statsmodels回归分析.ts
�?nbsp; �?nbsp; 10-10-高阶与分�c�d��量实�?ts
�?nbsp; �?nbsp; 11-11-案例�Q�汽车�h(hu��n)格预����Q务概�q?ts
�?nbsp; �?nbsp; 12-12-案例�Q�缺失值填�?ts
�?nbsp; �?nbsp; 13-13-案例�Q�特征相��x�?ts
�?nbsp; �?nbsp; 14-14-案例�Q�预处理问题.ts
�?nbsp; �?nbsp; 15-15-案例�Q�回归求�?ts
�?nbsp; �?/div>
�?nbsp; ├─12-假设���(g��)�?/div>
�?nbsp; �?nbsp; 01-1-假设���(g��)验基本思想.ts
�?nbsp; �?nbsp; 02-2-左右侧检验与双侧���(g��)�?ts
�?nbsp; �?nbsp; 03-3-Z���(g��)验基本原�?ts
�?nbsp; �?nbsp; 04-4-Z���(g��)验实�?ts
�?nbsp; �?nbsp; 05-5-T���(g��)验基本原�?ts
�?nbsp; �?nbsp; 06-6-T���(g��)验实�?ts
�?nbsp; �?nbsp; 07-7-T���(g��)验应用条�?ts
�?nbsp; �?nbsp; 08-8-卡方���(g��)�?ts
�?nbsp; �?nbsp; 09-9-假设���(g��)验中的两�c�错�?ts
�?nbsp; �?nbsp; 10-10-Python假设���(g��)验实�?ts
�?nbsp; �?nbsp; 11-11-Python卡方���(g��)验实�?ts
�?nbsp; �?/div>
�?nbsp; ├─13-相关分析
�?nbsp; �?nbsp; 01-1-相关分析概述.ts
�?nbsp; �?nbsp; 02-2-皮尔���相关系�?ts
�?nbsp; �?nbsp; 03-3-计算与检�?ts
�?nbsp; �?nbsp; 04-4-斯皮��?d��ng)曼�{���相关.ts
�?nbsp; �?nbsp; 05-5-肯�d��?d��ng)系�?ts
�?nbsp; �?nbsp; 06-6-质量相关分析.ts
�?nbsp; �?nbsp; 07-7-偏相关与复相�?ts
�?nbsp; �?/div>
�?nbsp; ├─14-方差分析
�?nbsp; �?nbsp; 01-1-方差分析概述.ts
�?nbsp; �?nbsp; 02-2-方差的比�?ts
�?nbsp; �?nbsp; 03-3-方差分析计算�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 04-4-方差分析中的多重比较.ts
�?nbsp; �?nbsp; 05-5-多因素方差分�?ts
�?nbsp; �?nbsp; 06-6-Python方差分析实例.ts
�?nbsp; �?/div>
�?nbsp; ├─15-聚类分析
�?nbsp; �?nbsp; 01-1-层次聚类概述.ts
�?nbsp; �?nbsp; 02-2-层次聚类���程.ts
�?nbsp; �?nbsp; 03-3-层次聚类实例.ts
�?nbsp; �?nbsp; 04-4-1-KMEANS���法概述.ts
�?nbsp; �?nbsp; 05-4-2-KMEANS工作���程.ts
�?nbsp; �?nbsp; 06-4-3-KMEANS�q�代可视化展�C?ts
�?nbsp; �?nbsp; 07-5-1-DBSCAN聚类���法.ts
�?nbsp; �?nbsp; 08-5-2-DBSCAN工作���程.ts
�?nbsp; �?nbsp; 09-5-3-DBSCAN可视化展�C?ts
�?nbsp; �?nbsp; 10-6-1-多种聚类���法概述.ts
�?nbsp; �?nbsp; 11-6-2-聚类案例实战.ts
�?nbsp; �?/div>
�?nbsp; └─16-贝叶斯分�?/div>
�?nbsp; 01-1-贝叶斯分析概�q?ts
�?nbsp; 02-2-概率的解�?ts
�?nbsp; 03-3-贝叶斯学�z�与�l�典�l�计学派的争�?ts
�?nbsp; 04-4-贝叶斯算法概�q?ts
�?nbsp; 05-5-贝叶斯推导实�?ts
�?nbsp; 06-6-贝叶斯拼写纠错实�?ts
�?nbsp; 07-7-垃圾邮�g�q���o(h��)实例.ts
�?nbsp; 08-8-贝叶斯解�?ts
�?nbsp; 09-9-�l�典求解思�\.ts
�?nbsp; 10-10-MCMC概述.ts
�?nbsp; 11-11-PYMC3概述.ts
�?nbsp; 12-12-模型诊断.ts
�?nbsp; 13-13-模型决策.ts
�?/div>
├─04-�W�四模块�Q�机器学�?f��n)算法精讲�?qi��ng)其案例应�?/div>
�?nbsp; ├─01-�U�性回归原理推�?/div>
�?nbsp; �?nbsp; 01-0-评�������?.ts
�?nbsp; �?nbsp; 02-1-回归问题概述.ts
�?nbsp; �?nbsp; 03-2-误差��定�?ts
�?nbsp; �?nbsp; 04-3-独立同分布的意义.ts
�?nbsp; �?nbsp; 05-4-似然函数的作�?ts
�?nbsp; �?nbsp; 06-5-参数求解.ts
�?nbsp; �?nbsp; 07-6-梯度下降通俗解释.ts
�?nbsp; �?nbsp; 08-7参数更新�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 09-8-优化参数讄���.ts
�?nbsp; �?/div>
�?nbsp; ├─02-�U�性回归代码实�?/div>
�?nbsp; �?nbsp; 01-�U�性回归整体模块概�q?ts
�?nbsp; �?nbsp; 02-初始化步�?ts
�?nbsp; �?nbsp; 03-实现梯度下降优化模块.ts
�?nbsp; �?nbsp; 04-损失与预���模�?ts
�?nbsp; �?nbsp; 05-数据与标�{�֮��?ts
�?nbsp; �?nbsp; 06-训练�U�性回归模�?ts
�?nbsp; �?nbsp; 07-得到�U�性回归方�E?ts
�?nbsp; �?nbsp; 08-整体���程debug解读.ts
�?nbsp; �?nbsp; 09-多特征回归模�?ts
�?nbsp; �?nbsp; 10-非线性回�?ts
�?nbsp; �?/div>
�?nbsp; ├─03-模型评估�Ҏ(gu��)��
�?nbsp; �?nbsp; 01-1-Sklearn工具包简�?ts
�?nbsp; �?nbsp; 02-2-数据集切�?ts
�?nbsp; �?nbsp; 03-3-交叉验证的作�?ts
�?nbsp; �?nbsp; 04-4-交叉验证实验分析.ts
�?nbsp; �?nbsp; 05-5-��h��矩阵.ts
�?nbsp; �?nbsp; 06-6-评估指标�Ҏ(gu��)��分析.ts
�?nbsp; �?nbsp; 07-7-阈值对�l�果的媄(ji��ng)�?ts
�?nbsp; �?nbsp; 08-8-ROC曲线.ts
�?nbsp; �?/div>
�?nbsp; ├─04-�U�性回归实验分�?/div>
�?nbsp; �?nbsp; 01-1-实验目标分析.ts
�?nbsp; �?nbsp; 02-2-参数直接求解�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 03-3-预处理对�l�果的媄(ji��ng)�?ts
�?nbsp; �?nbsp; 04-4-梯度下降模块.ts
�?nbsp; �?nbsp; 05-5-学习(f��n)率对�l�果的媄(ji��ng)�?ts
�?nbsp; �?nbsp; 06-6-随机梯度下降得到的效�?ts
�?nbsp; �?nbsp; 07-7-MiniBatch�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 08-8-不同�{�略效果�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 09-9-多项式回�?ts
�?nbsp; �?nbsp; 10-10-模型复杂�?ts
�?nbsp; �?nbsp; 11-11-��h��数量对结果的影响.ts
�?nbsp; �?nbsp; 12-12-正则化的作用.ts
�?nbsp; �?nbsp; 13-13-岭回归与lasso.mp4
�?nbsp; �?nbsp; 14-14-实验�ȝ��.ts
�?nbsp; �?/div>
�?nbsp; ├─05-逻辑回归实验分析
�?nbsp; �?nbsp; 01-1-逻辑回归���法原理.ts
�?nbsp; �?nbsp; 02-2-化简与求�?ts
�?nbsp; �?/div>
�?nbsp; ├─06-逻辑回归代码实现
�?nbsp; �?nbsp; 01-1-多分�c�逻辑回归整体思�\.ts
�?nbsp; �?nbsp; 02-2-训练模块功能.ts
�?nbsp; �?nbsp; 03-3-完成预测模块.ts
�?nbsp; �?nbsp; 04-4-优化目标定义.ts
�?nbsp; �?nbsp; 05-5-�q�代优化参数.ts
�?nbsp; �?nbsp; 06-6-梯度计算.ts
�?nbsp; �?nbsp; 07-7-得出最�l�结�?ts
�?nbsp; �?nbsp; 08-8-鸢尾花数据集多分�c�M�Q�?ts
�?nbsp; �?nbsp; 09-9-训练多分�c�L���?ts
�?nbsp; �?nbsp; 10-10-准备���试数据.ts
�?nbsp; �?nbsp; 11-11-决策边界�l�制.ts
�?nbsp; �?nbsp; 12-12-非线性决�{�边�?ts
�?nbsp; �?/div>
�?nbsp; ├─07-逻辑回归实验分析
�?nbsp; �?nbsp; 01-1-逻辑回归实验概述.ts
�?nbsp; �?nbsp; 02-2-概率�l�果随特征数值的变化.ts
�?nbsp; �?nbsp; 03-3-可视化展�C?ts
�?nbsp; �?nbsp; 04-4-坐标���盘制作.ts
�?nbsp; �?nbsp; 05-5-分类决策边界展示分析.ts
�?nbsp; �?nbsp; 06-6-多分�c?softmax.ts
�?nbsp; �?/div>
�?nbsp; ├─08-聚类���法-Kmeans&Dbscan原理
�?nbsp; �?nbsp; 01-1-KMEANS���法概述.ts
�?nbsp; �?nbsp; 02-2-KMEANS工作���程.ts
�?nbsp; �?nbsp; 03-3-KMEANS�q�代可视化展�C?ts
�?nbsp; �?nbsp; 04-4-DBSCAN聚类���法.ts
�?nbsp; �?nbsp; 05-5-DBSCAN工作���程.ts
�?nbsp; �?nbsp; 06-6-DBSCAN可视化展�C?ts
�?nbsp; �?/div>
�?nbsp; ├─09-Kmeans代码实现
�?nbsp; �?nbsp; 01-1-Kmeans���法模块概述.ts
�?nbsp; �?nbsp; 02-2-计算得到���中心点.ts
�?nbsp; �?nbsp; 03-3-��h��点归属划�?ts
�?nbsp; �?nbsp; 04-4-���法�q�代更新.ts
�?nbsp; �?nbsp; 05-5-鸢尾花数据集聚类��d��.ts
�?nbsp; �?nbsp; 06-6-聚类效果展示.ts
�?nbsp; �?/div>
�?nbsp; ├─10-聚类���法实验分析
�?nbsp; �?nbsp; 01-1-Kmenas���法常用操作.ts
�?nbsp; �?nbsp; 02-2-Kmenas���法常用操作_20190805_232034.ts
�?nbsp; �?nbsp; 03-1-聚类�l�果展示.ts
�?nbsp; �?nbsp; 04-2-聚类�l�果展示_20190805_232030.ts
�?nbsp; �?nbsp; 05-1-建模���程解读.ts
�?nbsp; �?nbsp; 06-2-建模���程解读_20190805_232032.ts
�?nbsp; �?nbsp; 07-2-不稳定结果_20190805_232028.ts
�?nbsp; �?nbsp; 08-1-不稳定结�?ts
�?nbsp; �?nbsp; 09-1-评估指标-Inertia.ts
�?nbsp; �?nbsp; 10-2-评估指标-Inertia_20190805_232027.ts
�?nbsp; �?nbsp; 11-1-如何扑ֈ�合适的K值_20190805_232026.ts
�?nbsp; �?nbsp; 12-2-如何扑ֈ�合适的K值_20190805_232026.ts
�?nbsp; �?nbsp; 13-2-Kmenas���法存在的问�?ts
�?nbsp; �?nbsp; 14-1-轮廓�p�L��的作用_20190805_232028.ts
�?nbsp; �?nbsp; 15-1-Kmenas���法存在的问题_20190805_232023.ts
�?nbsp; �?nbsp; 16-2-应用实例-囑փ�分割.ts
�?nbsp; �?nbsp; 17-1-应用实例-囑փ�分割_20190805_232021.ts
�?nbsp; �?nbsp; 18-2-半监督学�?f��n)_20190805_232033.ts
�?nbsp; �?nbsp; 19-1-半监督学�?ts
�?nbsp; �?nbsp; 20-1-DBSCAN���法.ts
�?nbsp; �?nbsp; 21-2-DBSCAN���法_20190805_232033.ts
�?nbsp; �?/div>
�?nbsp; ├─11-决策�?w��i)原�?/div>
�?nbsp; �?nbsp; 01-1-决策�?w��i)算法概�q?ts
�?nbsp; �?nbsp; 02-2-�늚�作用.ts
�?nbsp; �?nbsp; 03-3-信息增益原理.ts
�?nbsp; �?nbsp; 04-4-决策�?w��i)构造实�?ts
�?nbsp; �?nbsp; 05-5-信息增益率与gini�p�L��.ts
�?nbsp; �?nbsp; 06-6-预剪枝方�?ts
�?nbsp; �?nbsp; 07-7-后剪枝方�?ts
�?nbsp; �?nbsp; 08-8-回归问题解决.ts
�?nbsp; �?/div>
�?nbsp; ├─12-决策�?w��i)代码实�?/div>
�?nbsp; �?nbsp; 01-整体模块概述.ts
�?nbsp; �?nbsp; 02-递归生成�?w��i)节�?ts
�?nbsp; �?nbsp; 03-整体框架逻辑.ts
�?nbsp; �?nbsp; 04-熵��D����?ts
�?nbsp; �?nbsp; 05-数据集切�?ts
�?nbsp; �?nbsp; 06-完成�?w��i)模型构�?ts
�?nbsp; �?nbsp; 07-���试���法效果.ts
�?nbsp; �?/div>
�?nbsp; ├─13-决策�?w��i)实验分�?/div>
�?nbsp; �?nbsp; 01-1-�?w��i)模型可视化展�?ts
�?nbsp; �?nbsp; 02-2-决策边界展示分析.ts
�?nbsp; �?nbsp; 03-3-�?w��i)模型预剪枝参数作�?ts
�?nbsp; �?nbsp; 04-4-回归�?w��i)模�?ts
�?nbsp; �?/div>
�?nbsp; ├─14-集成���法原理
�?nbsp; �?nbsp; 01-1-随机���林���法原理.ts
�?nbsp; �?nbsp; 02-2-随机���林优势与特征重要性指�?ts
�?nbsp; �?nbsp; 03-3-提升���法概述.ts
�?nbsp; �?nbsp; 04-4-stacking堆叠模型.ts
�?nbsp; �?/div>
�?nbsp; ├─15-集成���法实验分析
�?nbsp; �?nbsp; 01-1-构徏实验数据�?ts
�?nbsp; �?nbsp; 02-2-���投���与软投���效果对�?ts
�?nbsp; �?nbsp; 03-3-Bagging�{�略效果.ts
�?nbsp; �?nbsp; 04-4-集成效果展示分析.ts
�?nbsp; �?nbsp; 05-5-OOB袋外数据的作�?ts
�?nbsp; �?nbsp; 06-6-特征重要性热度图展示.ts
�?nbsp; �?nbsp; 07-7-Adaboost���法概述.ts
�?nbsp; �?nbsp; 08-8-Adaboost决策边界效果.ts
�?nbsp; �?nbsp; 09-9-GBDT提升���法���程.ts
�?nbsp; �?nbsp; 10-10-集成参数�Ҏ(gu��)��分析.ts
�?nbsp; �?nbsp; 11-11-模型提前停止�{�略.ts
�?nbsp; �?nbsp; 12-12-停止�Ҏ(gu��)��实施.ts
�?nbsp; �?nbsp; 13-13-堆叠模型.ts
�?nbsp; �?/div>
�?nbsp; ├─16-支持向量机原理推�?/div>
�?nbsp; �?nbsp; 01-1-支持向量�����解决的问�?ts
�?nbsp; �?nbsp; 02-2-距离与数据定�?ts
�?nbsp; �?nbsp; 03-3-目标函数推导.ts
�?nbsp; �?nbsp; 04-4-拉格朗日乘子法求�?ts
�?nbsp; �?nbsp; 05-5-化简最�l�目标函�?ts
�?nbsp; �?nbsp; 06-6-求解决策方程.ts
�?nbsp; �?nbsp; 07-7-软间隔优�?ts
�?nbsp; �?nbsp; 08-8-核函数的作用.ts
�?nbsp; �?nbsp; 09-9-知识�Ҏ(gu��)�ȝ��.ts
�?nbsp; �?/div>
�?nbsp; ├─17-支持向量机实验分�?/div>
�?nbsp; �?nbsp; 01-1-支持向量机所能带来的效果.ts
�?nbsp; �?nbsp; 02-2-决策边界可视化展�C?ts
�?nbsp; �?nbsp; 03-3-软间隔的作用.ts
�?nbsp; �?nbsp; 04-4-非线性SVM.ts
�?nbsp; �?nbsp; 05-5-核函数的作用与效�?ts
�?nbsp; �?/div>
�?nbsp; ├─18-���经�|�络���法原理
�?nbsp; �?nbsp; 01-1-深度学习(f��n)要解决的问题.ts
�?nbsp; �?nbsp; 02-2-深度学习(f��n)应用领域.ts
�?nbsp; �?nbsp; 03-3-计算�����觉�Q�?ts
�?nbsp; �?nbsp; 04-4-视觉��d��中遇到的问题.ts
�?nbsp; �?nbsp; 05-5-得分函数.ts
�?nbsp; �?nbsp; 06-6-损失函数的作�?ts
�?nbsp; �?nbsp; 07-7-前向传播整体���程.ts
�?nbsp; �?nbsp; 08-8-�q�向传播计算�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 09-9-���经�|�络整体架构.ts
�?nbsp; �?nbsp; 10-10-���经�|�络架构�l�节.ts
�?nbsp; �?nbsp; 11-11-���经元个数对�l�果的媄(ji��ng)�?ts
�?nbsp; �?nbsp; 12-12-正则化与�Ȁ�z�d���?ts
�?nbsp; �?nbsp; 13-13-���经�|�络�q�拟合解��x���?ts
�?nbsp; �?/div>
�?nbsp; ├─19-���经�|�络代码实现
�?nbsp; �?nbsp; 01-1-���经�|�络整体框架概述.ts
�?nbsp; �?nbsp; 02-2-参数初始化操�?ts
�?nbsp; �?nbsp; 03-3-矩阵向量转换.ts
�?nbsp; �?nbsp; 04-4-向量反变�?ts
�?nbsp; �?nbsp; 05-5-完成前向传播模块.ts
�?nbsp; �?nbsp; 06-6-损失函数定义.ts
�?nbsp; �?nbsp; 07-7-准备反向传播�q�代.ts
�?nbsp; �?nbsp; 08-8-差异��计��?ts
�?nbsp; �?nbsp; 09-9-逐层计算.ts
�?nbsp; �?nbsp; 10-10-完成全部�q�代更新模块.ts
�?nbsp; �?nbsp; 11-11-手写字体识别数据�?ts
�?nbsp; �?nbsp; 12-12-���法代码错误修正.ts
�?nbsp; �?nbsp; 13-13-模型优化�l�果展示.ts
�?nbsp; �?nbsp; 14-14-���试效果可视化展�C?ts
�?nbsp; �?/div>
�?nbsp; ├─20-贝叶斯算法原�?/div>
�?nbsp; �?nbsp; 01-1-贝叶斯要解决的问�?ts
�?nbsp; �?nbsp; 02-2-贝叶斯公式推�?ts
�?nbsp; �?nbsp; 03-3-垃圾邮�g�q���o(h��)实例.ts
�?nbsp; �?nbsp; 04-4-拼写�U�错实例.ts
�?nbsp; �?/div>
�?nbsp; ├─21-贝叶斯代码实�?/div>
�?nbsp; �?nbsp; 01-1-朴素贝叶斯算法整体框�?ts
�?nbsp; �?nbsp; 02-2-邮�g数据��d��.ts
�?nbsp; �?nbsp; 03-3-预料表与特征向量构徏.ts
�?nbsp; �?nbsp; 04-4-分类别统计词�?ts
�?nbsp; �?nbsp; 05-5-贝叶斯公式对数变�?ts
�?nbsp; �?nbsp; 06-6-完成预测模块.ts
�?nbsp; �?/div>
�?nbsp; ├─22-兌���规则实战分析
�?nbsp; �?nbsp; 01-1-兌���规则概述.ts
�?nbsp; �?nbsp; 02-2-支持度与�|�信�?ts
�?nbsp; �?nbsp; 03-3-提升度的作用.ts
�?nbsp; �?nbsp; 04-4-Python实战兌���规则.ts
�?nbsp; �?nbsp; 05-5-数据集制�?ts
�?nbsp; �?nbsp; 06-6-�?sh��)�?ji��ng)数据集题材关联分�?ts
�?nbsp; �?/div>
�?nbsp; ├─23-兌���规则代码实现
�?nbsp; �?nbsp; 01-1-Apripri���法整体���程.ts
�?nbsp; �?nbsp; 02-2-数据集demo.ts
�?nbsp; �?nbsp; 03-3-扫描模块.ts
�?nbsp; �?nbsp; 04-4-拼接模块.ts
�?nbsp; �?nbsp; 05-5-挖掘频繁��w��.ts
�?nbsp; �?nbsp; 06-6-规则生成模块.ts
�?nbsp; �?nbsp; 07-7-完成全部���法���程.ts
�?nbsp; �?nbsp; 08-8-规则�l�果展示.ts
�?nbsp; �?/div>
�?nbsp; ├─24-词向量word2vec通俗解读
�?nbsp; �?nbsp; 01-1-词向量模型通俗解释.ts
�?nbsp; �?nbsp; 02-2-模型整体框架.ts
�?nbsp; �?nbsp; 03-3-训练数据构徏.ts
�?nbsp; �?nbsp; 04-4-CBOW与Skip-gram模型.ts
�?nbsp; �?nbsp; 05-5-负采��h���?ts
�?nbsp; �?/div>
�?nbsp; ├─25-代码实现word2vec词向量模�?/div>
�?nbsp; �?nbsp; 01-1-数据与�Q务流�E?ts
�?nbsp; �?nbsp; 02-2-数据清洗.ts
�?nbsp; �?nbsp; 03-3-batch数据制作.ts
�?nbsp; �?nbsp; 04-4-�|�络训练.ts
�?nbsp; �?nbsp; 05-5-可视化展�C?ts
�?nbsp; �?/div>
�?nbsp; ├─26-�U�性判别分析降�l�算法原理解�?/div>
�?nbsp; �?nbsp; 01-1-�U�性判别分析要解决的问�?ts
�?nbsp; �?nbsp; 02-2-�U�性判别分析要优化的目�?ts
�?nbsp; �?nbsp; 03-3-�U�性判别分析求�?ts
�?nbsp; �?nbsp; 04-4-实现�U�性判别分析进行降�l��Q�?ts
�?nbsp; �?nbsp; 05-5-求解得出降维�l�果.ts
�?nbsp; �?/div>
�?nbsp; ├─27-��L��分分析降�l�算法原理解�?/div>
�?nbsp; �?nbsp; 01-1-PCA基本概念.ts
�?nbsp; �?nbsp; 02-2-方差与协方差.ts
�?nbsp; �?nbsp; 03-3-PCA�l�果推导.ts
�?nbsp; �?nbsp; 04-4-PCA降维实例.ts
�?nbsp; �?/div>
�?nbsp; ├─28-隐马��?d��ng)科夫模�?/div>
�?nbsp; �?nbsp; 01-1-马尔�U�夫模型.ts
�?nbsp; �?nbsp; 02-2-隐马��?d��ng)科夫模型基本出发�?ts
�?nbsp; �?nbsp; 03-3-�l�成与要解决的问�?ts
�?nbsp; �?nbsp; 04-4-暴力求解�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 05-5-复杂度计��?ts
�?nbsp; �?nbsp; 06-6-前向���法.ts
�?nbsp; �?nbsp; 07-7-前向���法求解实例.ts
�?nbsp; �?nbsp; 08-8-Baum-Welch���法.ts
�?nbsp; �?nbsp; 09-9-参数求解.ts
�?nbsp; �?nbsp; 10-10-�l�特比算�?ts
�?nbsp; �?/div>
�?nbsp; └─29-HMM应用实例
�?nbsp; 01-1-hmmlearn工具�?ts
�?nbsp; 02-2-工具包��用方�?ts
�?nbsp; 03-3-中文分词��d��.ts
�?nbsp; 04-4-实现中文分词.ts
�?/div>
├─05-�W�五模块�Q�机器学�?f��n)算法徏模实战项�?/div>
�?nbsp; ├─01-��目实战-交易数据异常���(g��)��?/div>
�?nbsp; �?nbsp; 01-1-��d��目标解读.ts
�?nbsp; �?nbsp; 02-2-��目挑战与解��x��案制�?ts
�?nbsp; �?nbsp; 03-3-数据标准化处�?ts
�?nbsp; �?nbsp; 04-4-下采��h��据集制作.ts
�?nbsp; �?nbsp; 05-5-交叉验证.ts
�?nbsp; �?nbsp; 06-6-数据集切�?ts
�?nbsp; �?nbsp; 07-7-模型评估�Ҏ(gu��)��与召回率.ts
�?nbsp; �?nbsp; 08-8-正则化惩�|�项.ts
�?nbsp; �?nbsp; 09-9-训练逻辑回归模型.ts
�?nbsp; �?nbsp; 10-10-��h��矩阵评估分析.ts
�?nbsp; �?nbsp; 11-11-���试集遇到的问题.ts
�?nbsp; �?nbsp; 12-12-阈值对�l�果的媄(ji��ng)�?ts
�?nbsp; �?nbsp; 13-13-SMOTE��h��生成�{�略.ts
�?nbsp; �?nbsp; 14-14-�q�采��h��果与��目�ȝ��.ts
�?nbsp; �?/div>
�?nbsp; ├─02-��Z��随机���林的气温预���实�?/div>
�?nbsp; �?nbsp; 01-1-��Z��随机���林的气温预����Q务概�q?ts
�?nbsp; �?nbsp; 02-2-基本随机���林模型建立.ts
�?nbsp; �?nbsp; 03-3-可视化展�C�Z��特征重要�?ts
�?nbsp; �?nbsp; 04-4-加入新的数据与特�?ts
�?nbsp; �?nbsp; 05-5-数据与特征对�l�果的媄(ji��ng)�?ts
�?nbsp; �?nbsp; 06-6-效率�Ҏ(gu��)��分析.ts
�?nbsp; �?nbsp; 07-7-�|�格与随机参数选择.ts
�?nbsp; �?nbsp; 08-8-随机参数选择�Ҏ(gu��)��实践.ts
�?nbsp; �?nbsp; 09-9-调参优化�l�节.ts
�?nbsp; �?/div>
�?nbsp; ├─03-贝叶斯新��d���c�d���?/div>
�?nbsp; �?nbsp; 01-1-新闻数据与�Q务概�q?ts
�?nbsp; �?nbsp; 02-2-中文分词与停用词�q���o(h��).ts
�?nbsp; �?nbsp; 03-3-文本关键词提�?ts
�?nbsp; �?nbsp; 04-4-词袋模型.ts
�?nbsp; �?nbsp; 05-5-贝叶斯徏模结�?ts
�?nbsp; �?nbsp; 06-6-TF-IDF特征分析�Ҏ(gu��)��.ts
�?nbsp; �?/div>
�?nbsp; ├─04-推荐�pȝ��实战
�?nbsp; �?nbsp; 01-1-音乐推荐��d��概述.ts
�?nbsp; �?nbsp; 02-2-数据集整�?ts
�?nbsp; �?nbsp; 03-3-��Z��物品的协同过�?ts
�?nbsp; �?nbsp; 04-4-物品�怼�度计���与推荐.ts
�?nbsp; �?nbsp; 05-5-SVD矩阵分解.ts
�?nbsp; �?nbsp; 06-6-��Z��矩阵分解的音乐推�?ts
�?nbsp; �?/div>
�?nbsp; ├─05-fbprophe旉���序列预测
�?nbsp; �?nbsp; 01-1-fbprophet股�h(hu��n)预测��d��概述.ts
�?nbsp; �?nbsp; 02-2-旉���序列分析.ts
�?nbsp; �?nbsp; 03-3-fbprophet旉���序列预测实例.mp4
�?nbsp; �?nbsp; 04-4-亚马逊股仯����?mp4
�?nbsp; �?nbsp; 05-5-�H�变点调�?ts
�?nbsp; �?/div>
�?nbsp; └─06-京东用户购买意向预测
�?nbsp; 01-1-��目与数据介�l?ts
�?nbsp; 02-2-数据挖掘?g��u)���?ts
�?nbsp; 03-3-数据���(g��)�?ts
�?nbsp; 04-4-构徏用户特征表单.ts
�?nbsp; 05-5-构徏商品特征表单.ts
�?nbsp; 06-6-数据探烦(ch��)概述.ts
�?nbsp; 07-7-购买因素分析.ts
�?nbsp; 08-8-特征工程.ts
�?nbsp; 09-9-基本特征构�?ts
�?nbsp; 10-10-行�ؓ(f��)特征.ts
�?nbsp; 11-11-累积行�ؓ(f��)特征.ts
�?nbsp; 12-12-Xgboost模型.ts
�?/div>
├─06-�W�六模块�Q�机器学�?f��n)案例实战应用集�?/div>
�?nbsp; ├─01-Python实战兌���规则
�?nbsp; �?nbsp; 01-1-兌���规则概述.ts
�?nbsp; �?nbsp; 02-2-支持度与�|�信�?ts
�?nbsp; �?nbsp; 03-3-提升度的作用.ts
�?nbsp; �?nbsp; 04-4-Python实战兌���规则.ts
�?nbsp; �?nbsp; 05-5-数据集制�?ts
�?nbsp; �?nbsp; 06-6-�?sh��)�?ji��ng)数据集题材关联分�?ts
�?nbsp; �?/div>
�?nbsp; ├─02-爱彼�q�数据集分析与徏�?/div>
�?nbsp; �?nbsp; 01-1-数据与�Q务分�?ts
�?nbsp; �?nbsp; 02-2-提取月䆾信息�q�行�l�计分析.ts
�?nbsp; �?nbsp; 03-3-房�h(hu��n)随星期变化的可视化展�C?ts
�?nbsp; �?nbsp; 04-4-房屋信息指标分析.ts
�?nbsp; �?nbsp; 05-5-提取房屋常见设施.ts
�?nbsp; �?nbsp; 06-6-房屋规格热度囑ֈ��?ts
�?nbsp; �?nbsp; 07-7-预处理与建模准备.ts
�?nbsp; �?nbsp; 08-8-随机���林与LightGBM.ts
�?nbsp; �?nbsp; 09-9-训练与评�?ts
�?nbsp; �?/div>
�?nbsp; ├─03-��Z���怼�度的酒店推荐�pȝ��
�?nbsp; �?nbsp; 01-1-数据与�Q务介�l?ts
�?nbsp; �?nbsp; 02-2-文本词频�l�计.ts
�?nbsp; �?nbsp; 03-3-ngram�l�果可视化展�C?ts
�?nbsp; �?nbsp; 04-4-文本清洗.ts
�?nbsp; �?nbsp; 05-5-�怼�度计��?ts
�?nbsp; �?nbsp; 06-6-得出推荐�l�果.ts
�?nbsp; �?/div>
�?nbsp; ├─04-商品销售额回归分析
�?nbsp; �?nbsp; 01-1-数据��d��分析.ts
�?nbsp; �?nbsp; 02-2-特征工程制作.ts
�?nbsp; �?nbsp; 03-3-�l�计指标生成.ts
�?nbsp; �?nbsp; 04-4-特征信息提取.ts
�?nbsp; �?nbsp; 05-5-标签变换.ts
�?nbsp; �?nbsp; 06-6-输入数据制作.ts
�?nbsp; �?nbsp; 07-7-Xgboost训练模型.ts
�?nbsp; �?nbsp; 08-8-生成输出�l�果.ts
�?nbsp; �?/div>
�?nbsp; ├─05-�l�地求生数据集探索分析与建模
�?nbsp; �?nbsp; 01-1-数据与�Q务简�?ts
�?nbsp; �?nbsp; 02-2-数据问题探烦(ch��)与解��x���?ts
�?nbsp; �?nbsp; 03-3-剔除开挂数�?ts
�?nbsp; �?nbsp; 04-5-�l�图�l�计分析.ts
�?nbsp; �?nbsp; 05-6-热度囑ֱ��C?ts
�?nbsp; �?nbsp; 06-7-随机���林建模.ts
�?nbsp; �?nbsp; 07-8-特征重要�?ts
�?nbsp; �?/div>
�?nbsp; ├─06-机器学习(f��n)-模型解释�Ҏ(gu��)��实战
�?nbsp; �?nbsp; 01-1-模型解释�Ҏ(gu��)��与实�?ts
�?nbsp; �?nbsp; 02-2-部分依赖图解�?ts
�?nbsp; �?nbsp; 03-3-双变量分�?ts
�?nbsp; �?nbsp; 04-4-ShapValues指标分析.ts
�?nbsp; �?nbsp; 05-5-疄���引�v原因分析实战.ts
�?nbsp; �?/div>
�?nbsp; ├─07-自然语言处理必备工具包实�?/div>
�?nbsp; �?nbsp; 01-1-Python字符串处�?ts
�?nbsp; �?nbsp; 02-2-正则表达式基本语�?ts
�?nbsp; �?nbsp; 03-3-正则常用�W�号.ts
�?nbsp; �?nbsp; 04-4-常用函数介绍.ts
�?nbsp; �?nbsp; 05-5-NLTK工具包简�?ts
�?nbsp; �?nbsp; 06-6-停用词过�?ts
�?nbsp; �?nbsp; 07-7-词性标�?ts
�?nbsp; �?nbsp; 08-8-数据清洗实例.ts
�?nbsp; �?nbsp; 09-9-Spacy工具�?ts
�?nbsp; �?nbsp; 10-10-名字实体匚w��.ts
�?nbsp; �?nbsp; 11-11-恐怖袭��d���?ts
�?nbsp; �?nbsp; 12-12-�l�计分析�l�果.ts
�?nbsp; �?nbsp; 13-13-�l�巴分词�?ts
�?nbsp; �?nbsp; 14-14-词云展示.ts
�?nbsp; �?/div>
�?nbsp; ├─08-NLP核心模型-Word2vec
�?nbsp; �?nbsp; 01-1-词向量模型通俗解释.ts
�?nbsp; �?nbsp; 02-2-模型整体框架.ts
�?nbsp; �?nbsp; 03-3-训练数据构徏.ts
�?nbsp; �?nbsp; 04-4-CBOW与Skip-gram模型.ts
�?nbsp; �?nbsp; 05-5-负采��h���?ts
�?nbsp; �?/div>
�?nbsp; ├─09-数据特征预处�?/div>
�?nbsp; �?nbsp; 01-1-��d��概述.ts
�?nbsp; �?nbsp; 02-2-词袋模型.ts
�?nbsp; �?nbsp; 03-3-词袋模型分析.ts
�?nbsp; �?nbsp; 04-4-TFIDF模型.ts
�?nbsp; �?nbsp; 05-5-word2vec词向量模�?ts
�?nbsp; �?nbsp; 06-6-深度学习(f��n)模型.ts
�?nbsp; �?/div>
�?nbsp; ├─10-10文本特征处理�Ҏ(gu��)���Ҏ(gu��)��
�?nbsp; �?nbsp; 01-1-数据与�Q务介�l?ts
�?nbsp; �?nbsp; 02-2-数据分析与可视化展示.ts
�?nbsp; �?nbsp; 03-3-�q�箋值离散化与可视化�l�节.ts
�?nbsp; �?nbsp; 04-4-加蝲数据坐标到实际地图中�q�行分析.ts
�?nbsp; �?nbsp; 05-5-特征相关性分�?ts
�?nbsp; �?nbsp; 06-6-�~�失值填�?ts
�?nbsp; �?nbsp; 07-7-sklearn工具包预处理模块.ts
�?nbsp; �?nbsp; 08-8-���L��属性特征处�?ts
�?nbsp; �?nbsp; 09-9-构徏合适的特征.ts
�?nbsp; �?nbsp; 10-10-序列化执行预处理操作.ts
�?nbsp; �?nbsp; 11-11-完成所有预处理操作.ts
�?nbsp; �?nbsp; 12-12-构徏回归模型.ts
�?nbsp; �?/div>
�?nbsp; ├─11-银行客户�q�款可能性预��?/div>
�?nbsp; �?nbsp; 01-1-数据��d��介绍�?qi��ng)缺失值处�?ts
�?nbsp; �?nbsp; 02-2-EDA数据探烦(ch��)分析.ts
�?nbsp; �?nbsp; 03-3-特征展示分析.ts
�?nbsp; �?nbsp; 04-4-KDEPLOT展示.ts
�?nbsp; �?nbsp; 05-5-部分特征分析与可视化.ts
�?nbsp; �?nbsp; 06-6-数据���(g��)查与特征工程.ts
�?nbsp; �?nbsp; 07-7-多项式特�?ts
�?nbsp; �?nbsp; 08-8-自定义特�?ts
�?nbsp; �?nbsp; 09-9-逻辑回归模型.ts
�?nbsp; �?nbsp; 10-10-�l�果评估.ts
�?nbsp; �?nbsp; 11-11-必杀���奇�Q�lightgbm.ts
�?nbsp; �?/div>
�?nbsp; └─12-囑փ�特征聚类分析实践
�?nbsp; 01-1-数据与�Q务流�E�分�?ts
�?nbsp; 02-2-囄���数据导入.ts
�?nbsp; 03-3-囑փ�特征�~�码.ts
�?nbsp; 04-4-数组保存与读�?ts
�?nbsp; 05-5-得出聚类�l�果.ts
�?nbsp; 06-6-聚类效果可视化展�C?ts
�?/div>
├─07-�W�七模块�Q�机器学�?f��n)竞赛优胜解��x��案实�?/div>
�?nbsp; ├─01-快手短视频用��h��跃度分析
�?nbsp; �?nbsp; 01-1-��d��目标与数据分�?.ts
�?nbsp; �?nbsp; 02-2-整体模型架构.ts
�?nbsp; �?nbsp; 03-3-构徏用户特征序列.ts
�?nbsp; �?nbsp; 04-4-序列特征提取�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 05-5-生成特征汇总表.ts
�?nbsp; �?nbsp; 06-6-标签制作.ts
�?nbsp; �?nbsp; 07-7-�|�络训练模块.ts
�?nbsp; �?nbsp; 08-8-得出最�l�模型结�?ts
�?nbsp; �?nbsp; 09-0-评�������?ts
�?nbsp; �?/div>
�?nbsp; ├─02-工业化生产预��?/div>
�?nbsp; �?nbsp; 01-1-数据��d��概述.ts
�?nbsp; �?nbsp; 02-2-数据异常���(g��)�?ts
�?nbsp; �?nbsp; 03-3-旉���特征提取.ts
�?nbsp; �?nbsp; 04-4-各道工序特征构徏.ts
�?nbsp; �?nbsp; 05-5-准备训练数据.ts
�?nbsp; �?nbsp; 06-6-训练xgboost模型.ts
�?nbsp; �?/div>
�?nbsp; ├─03-智慧城市-道�\通行旉���预测
�?nbsp; �?nbsp; 01-1-数据与�Q务目标分�?ts
�?nbsp; �?nbsp; 02-2-数据清洗与标�{��{�?ts
�?nbsp; �?nbsp; 03-3-道�\通行旉���序列数据生成.ts
�?nbsp; �?nbsp; 04-4-序列�~�失补全�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 05-5-��Z��回归与插值完成序列特�?ts
�?nbsp; �?nbsp; 06-6-��Z��回归与插��D��行序列补�?ts
�?nbsp; �?nbsp; 07-7-特征汇�?ts
�?nbsp; �?nbsp; 08-8-建立回归模型�q�行预测.ts
�?nbsp; �?/div>
�?nbsp; ├─04-特征工程建模可解释包
�?nbsp; �?nbsp; 01-1-模型解释�Ҏ(gu��)��与实�?ts
�?nbsp; �?nbsp; 02-2-部分依赖图解�?ts
�?nbsp; �?nbsp; 03-3-双变量分�?ts
�?nbsp; �?nbsp; 04-4-ShapValues指标分析.ts
�?nbsp; �?nbsp; 05-5-疄���引�v原因分析实战.ts
�?nbsp; �?nbsp; 06-1-竞赛与目标分�?ts
�?nbsp; �?nbsp; 07-1-特征�Ҏ(gu��)��分析�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 08-1-�l�果�Ҏ(gu��)��分析.ts
�?nbsp; �?/div>
�?nbsp; ├─05-��d���p�尿病数据命名实体识�?/div>
�?nbsp; �?nbsp; 01-1-数据与�Q务介�l?ts
�?nbsp; �?nbsp; 02-2-整体模型架构.ts
�?nbsp; �?nbsp; 03-3-数据-标签-语料库处�?ts
�?nbsp; �?nbsp; 04-4-输入��h��填充补齐.ts
�?nbsp; �?nbsp; 05-5-训练�|�络模型.ts
�?nbsp; �?nbsp; 06-6-�ȝ��数据集(�p�尿病)(j��)实体识别.ts
�?nbsp; �?/div>
�?nbsp; ├─06-��h���q�_��风控模型+特征工程
�?nbsp; �?nbsp; 01-1-竞赛��d��目标.ts
�?nbsp; �?nbsp; 02-2-图模型信息提�?ts
�?nbsp; �?nbsp; 03-3-节点权重特征提取(PageRank).ts
�?nbsp; �?nbsp; 04-4-deepwalk构徏��N��点特�?ts
�?nbsp; �?nbsp; 05-5-各项�l�计特征.ts
�?nbsp; �?nbsp; 06-6-app安装特征.ts
�?nbsp; �?nbsp; 07-7-图中联系人特�?ts
�?nbsp; �?/div>
�?nbsp; ├─07-新闻关键词抽取模�?/div>
�?nbsp; �?nbsp; 01-1-��d��目标与数据集介绍.ts
�?nbsp; �?nbsp; 02-2-数据清洗与预处理.ts
�?nbsp; �?nbsp; 03-3-基本特征抽取.ts
�?nbsp; �?nbsp; 04-4-文章与词向量分析.ts
�?nbsp; �?nbsp; 05-5-权重划分.ts
�?nbsp; �?nbsp; 06-6-候选词�l�计特征.ts
�?nbsp; �?nbsp; 07-7-textrank特征提取.ts
�?nbsp; �?nbsp; 08-8-候选词�怼�度特�?ts
�?nbsp; �?nbsp; 09-9-特征工程汇�?ts
�?nbsp; �?/div>
�?nbsp; ├─08-数据特征常用构徏�Ҏ(gu��)��
�?nbsp; �?nbsp; 01-1-基本数值特�?ts
�?nbsp; �?nbsp; 02-2-常用特征构造手�D?ts
�?nbsp; �?nbsp; 03-3-旉���特征处理.ts
�?nbsp; �?nbsp; 04-4-文本特征处理.ts
�?nbsp; �?nbsp; 05-5-构造文本向�?ts
�?nbsp; �?nbsp; 06-6-词向量特�?ts
�?nbsp; �?nbsp; 07-7-计算机眼中的囑փ�.ts
�?nbsp; �?/div>
�?nbsp; ├─09-用电(sh��)敏感客户分类
�?nbsp; �?nbsp; 01-1-��d��与解��x��架概�q?ts
�?nbsp; �?nbsp; 02-2-特征工程分析与特征提�?ts
�?nbsp; �?nbsp; 03-3-���L��数据处理.ts
�?nbsp; �?nbsp; 04-4-�l�计与文本特�?ts
�?nbsp; �?nbsp; 05-5-文本特征构徏.ts
�?nbsp; �?nbsp; 06-6-构徏低敏用户模型.ts
�?nbsp; �?nbsp; 07-7-高敏模型概述.ts
�?nbsp; �?/div>
�?nbsp; └─10-机器学习(f��n)��目实战模板
�?nbsp; 01-1-建筑能源利用效率��d��概述.ts
�?nbsp; 02-2-处理���程与数据简�?ts
�?nbsp; 03-3-能源信息各项指标数据预处�?ts
�?nbsp; 04-4-单变量绘囑ֈ��?ts
�?nbsp; 05-5-��ȝ��点剔�?ts
�?nbsp; 06-6-变量与结果的关系.ts
�?nbsp; 07-7-多变量展�C?ts
�?nbsp; 08-8-特征工程的�h(hu��n)值和�Ҏ(gu��)��.ts
�?nbsp; 09-1-dataleakage问题.ts
�?nbsp; 10-2-基础模型�Ҏ(gu��)��.ts
�?nbsp; 11-3-选择参数.ts
�?nbsp; 12-4-���试模型.ts
�?nbsp; 13-5-模型解释.ts
�?nbsp; 14-6-模型分析.ts
�?/div>
├─08-�W�八模块�Q�Python金融分析与量化交易实�?/div>
�?nbsp; ├─01-评���内容与大�U�介�l?/div>
�?nbsp; �?nbsp; 01-评���内容与大�U�介�l?ts
�?nbsp; �?/div>
�?nbsp; ├─02-金融数据旉���序列分析
�?nbsp; �?nbsp; 01-1-金融旉���序列数据�l�计分析.ts
�?nbsp; �?nbsp; 02-2-序列变化情况分析计算.ts
�?nbsp; �?nbsp; 03-3-�q�箋指标变化情况分析.ts
�?nbsp; �?nbsp; 04-4-旉���序列重采��h���?ts
�?nbsp; �?nbsp; 05-5-短均与长均计���实�?ts
�?nbsp; �?nbsp; 06-6-指标相关情况分析.ts
�?nbsp; �?nbsp; 07-7-回归方程与相关系数实�?ts
�?nbsp; �?/div>
�?nbsp; ├─03-1双均�U�交易策略实�?/div>
�?nbsp; �?nbsp; 01-1-金叉与死叉介�l?ts
�?nbsp; �?nbsp; 02-2-买点与卖点可视化分析.ts
�?nbsp; �?nbsp; 03-3-�{�略收益效果分析.ts
�?nbsp; �?nbsp; 04-4-均线调参实例.ts
�?nbsp; �?/div>
�?nbsp; ├─04-�{�略收益与风险评估指标解�?/div>
�?nbsp; �?nbsp; 01-1-回测收益率指标解�?ts
�?nbsp; �?nbsp; 02-1-回测收益率指标解�?ts
�?nbsp; �?nbsp; 03-3-最大回撤区�?ts
�?nbsp; �?nbsp; 04-4-夏普比率的作�?ts
�?nbsp; �?nbsp; 05-5-阿尔法与贝塔概述.ts
�?nbsp; �?/div>
�?nbsp; ├─05-量化交易与回�����^台解�?/div>
�?nbsp; �?nbsp; 01-1-量化交易概述.ts
�?nbsp; �?nbsp; 02-2-量化交易所需技能分�?ts
�?nbsp; �?nbsp; 03-3-Ricequant交易�q�_������?ts
�?nbsp; �?/div>
�?nbsp; ├─06-Ricequant回测选股分析实战
�?nbsp; �?nbsp; 01-1-�{�略��d��分析.ts
�?nbsp; �?nbsp; 02-2-股票池筛�?ts
�?nbsp; �?nbsp; 03-2-股票池筛�?ts
�?nbsp; �?nbsp; 04-4-定时器功能与作用.ts
�?nbsp; �?/div>
�?nbsp; ├─07-因子数据预处理实�?/div>
�?nbsp; �?nbsp; 01-1-癑ֈ�位去极值方�?ts
�?nbsp; �?nbsp; 02-2-��Z��癑ֈ�位去极值实�?ts
�?nbsp; �?nbsp; 03-3-Mad法去极值演�C?ts
�?nbsp; �?nbsp; 04-4-3Sigma�Ҏ(gu��)��实例.ts
�?nbsp; �?nbsp; 05-5-标准化处理方�?ts
�?nbsp; �?nbsp; 06-6-中性化处理�Ҏ(gu��)��通俗解释.ts
�?nbsp; �?nbsp; 07-7-�{�略��d��概述.ts
�?nbsp; �?/div>
�?nbsp; ├─08-因子选股�{�略实战
�?nbsp; �?nbsp; 01-1-股票数据获取.ts
�?nbsp; �?nbsp; 02-2-�q���o(h��)�{�选因子指标数�?ts
�?nbsp; �?nbsp; 03-3-因子数据预处�?ts
�?nbsp; �?nbsp; 04-4-股票池筛�?ts
�?nbsp; �?nbsp; 05-5-�{�略效果评估分析.ts
�?nbsp; �?/div>
�?nbsp; ├─09-因子分析实战
�?nbsp; �?nbsp; 01-5-�{�略效果评估分析.ts
�?nbsp; �?nbsp; 02-2-Alphalens工具包介�l?ts
�?nbsp; �?nbsp; 03-3-获取因子指标数据.ts
�?nbsp; �?nbsp; 04-4-获取�l�定区间全部数据.ts
�?nbsp; �?nbsp; 05-5-数据格式转换.ts
�?nbsp; �?nbsp; 06-6-IC指标��D����?ts
�?nbsp; �?nbsp; 07-7-工具包绘囑ֱ��C?ts
�?nbsp; �?nbsp; 08-8-因子收益率简�?ts
�?nbsp; �?/div>
�?nbsp; ├─10-因子打分选股实战
�?nbsp; �?nbsp; 01-1-打分法选股�{�略概述.ts
�?nbsp; �?nbsp; 02-2-整体��d�����程梳理.ts
�?nbsp; �?nbsp; 03-3-�{�略初始化与数据��d��.ts
�?nbsp; �?nbsp; 04-4-因子打分与排�?ts
�?nbsp; �?nbsp; 05-5-完成选股�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 06-6-完成�{�略交易展示�l�果.ts
�?nbsp; �?nbsp; 07-7-�{�略�ȝ��与分�?ts
�?nbsp; �?/div>
�?nbsp; ├─11-回归分析�{�略
�?nbsp; �?nbsp; 01-1-回归问题概述.ts
�?nbsp; �?nbsp; 02-2-误差��定�?ts
�?nbsp; �?nbsp; 03-3-独立同分布的意义.ts
�?nbsp; �?nbsp; 04-4-似然函数的作�?ts
�?nbsp; �?nbsp; 05-5-参数求解.ts
�?nbsp; �?nbsp; 06-6-梯度下降通俗解释.ts
�?nbsp; �?nbsp; 07-7参数更新�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 08-8-优化参数讄���.ts
�?nbsp; �?nbsp; 09-9-回归��d��概述.ts
�?nbsp; �?nbsp; 10-10-特征可视化展�C?ts
�?nbsp; �?nbsp; 11-11-构徏回归方程.ts
�?nbsp; �?nbsp; 12-12-回归分析�l�果.ts
�?nbsp; �?/div>
�?nbsp; ├─11-聚类分析�{�略
�?nbsp; �?nbsp; 01-1-KMEANS���法概述.ts
�?nbsp; �?nbsp; 02-2-KMEANS工作���程.ts
�?nbsp; �?nbsp; 03-3-KMEANS�q�代可视化展�C?ts
�?nbsp; �?nbsp; 04-4-DBSCAN聚类���法.ts
�?nbsp; �?nbsp; 05-5-DBSCAN工作���程.ts
�?nbsp; �?nbsp; 06-6-DBSCAN可视化展�C?ts
�?nbsp; �?nbsp; 07-6-DBSCAN可视化展�C?ts
�?nbsp; �?nbsp; 08-8-�l�计分析所需数据准备.ts
�?nbsp; �?nbsp; 09-9-�l�计效果展示.ts
�?nbsp; �?/div>
�?nbsp; ├─12-拓展�Q�fbprophet旉���序列预测���器
�?nbsp; �?nbsp; 01-1-fbprophet股�h(hu��n)预测��d��概述.ts
�?nbsp; �?nbsp; 02-2-旉���序列分析.ts
�?nbsp; �?nbsp; 03-3-fbprophet旉���序列预测实例.mp4
�?nbsp; �?nbsp; 04-4-亚马逊股仯����?ts
�?nbsp; �?nbsp; 05-5-�H�变点调�?ts
�?nbsp; �?/div>
�?nbsp; └─13-��Z��深度学习(f��n)的时间序列预��?/div>
�?nbsp; 01-1-��d��目标与数据源.ts
�?nbsp; 02-2-构徏旉���序列数据.ts
�?nbsp; 03-3-训练旉���序列数据预测�l�果.ts
�?nbsp; 04-4-多特征预���结�?ts
�?nbsp; 05-5-序列�l�果预测.ts
�?/div>
├─09-�W�九(ji��)模块�Q�深度学�?f��n)经典算法解�?/div>
�?nbsp; ├─01-深度学习(f��n)必备基础知识点础
�?nbsp; �?nbsp; 01-1-深度学习(f��n)要解决的问题.ts
�?nbsp; �?nbsp; 02-2-深度学习(f��n)应用领域.ts
�?nbsp; �?nbsp; 03-3-计算�����觉�Q�?ts
�?nbsp; �?nbsp; 04-4-视觉��d��中遇到的问题.ts
�?nbsp; �?nbsp; 05-5-得分函数.ts
�?nbsp; �?nbsp; 06-6-损失函数的作�?ts
�?nbsp; �?nbsp; 07-7-前向传播整体���程.ts
�?nbsp; �?/div>
�?nbsp; ├─02-���经�|�络整体架构
�?nbsp; �?nbsp; 01-1-�q�向传播计算�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 02-2-���经�|�络整体架构.ts
�?nbsp; �?nbsp; 03-2-���经�|�络整体架构.ts
�?nbsp; �?nbsp; 04-4-���经元个数对�l�果的媄(ji��ng)�?ts
�?nbsp; �?nbsp; 05-5-正则化与�Ȁ�z�d���?ts
�?nbsp; �?nbsp; 06-6-���经�|�络�q�拟合解��x���?ts
�?nbsp; �?/div>
�?nbsp; ├─03-��L(f��ng)�����经�|�络原理与参数解�?/div>
�?nbsp; �?nbsp; 01-1-��L(f��ng)�����经�|�络应用领域.ts
�?nbsp; �?nbsp; 02-2-��L(f��ng)��的作�?ts
�?nbsp; �?nbsp; 03-3-��L(f��ng)��特征��D�����方�?ts
�?nbsp; �?nbsp; 04-4-得到特征图表�C?ts
�?nbsp; �?nbsp; 05-5-步长与卷�U�核大小对结果的影响.ts
�?nbsp; �?nbsp; 06-6-边缘填充�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 07-7-特征囑ְ�寸计���与参数�׃�n.ts
�?nbsp; �?nbsp; 08-8-池化层的作用.ts
�?nbsp; �?nbsp; 09-9-整体�|�络架构.ts
�?nbsp; �?nbsp; 10-10-VGG�|�络架构.ts
�?nbsp; �?nbsp; 11-11-�D�差�|�络Resnet.ts
�?nbsp; �?nbsp; 12-12-感受野的作用.ts
�?nbsp; �?/div>
�?nbsp; ├─04-递归���经�|�络与词向量原理解读
�?nbsp; �?nbsp; 01-12-感受野的作用.ts
�?nbsp; �?nbsp; 02-2-词向量模型通俗解释.ts
�?nbsp; �?nbsp; 03-3-模型整体框架.ts
�?nbsp; �?nbsp; 04-4-训练数据构徏.ts
�?nbsp; �?nbsp; 05-5-CBOW与Skip-gram模型.ts
�?nbsp; �?nbsp; 06-6-负采��h���?ts
�?nbsp; �?/div>
�?nbsp; ├─05-案例实战搭徏���经�|�络
�?nbsp; �?nbsp; 01-0-keras框架���介与安装.ts
�?nbsp; �?nbsp; 02-1-训练自己的数据集整体���程.ts
�?nbsp; �?nbsp; 03-2-数据加蝲与预处理.ts
�?nbsp; �?nbsp; 04-3-搭徏�|�络模型.ts
�?nbsp; �?nbsp; 05-4-学习(f��n)率对�l�果的媄(ji��ng)�?ts
�?nbsp; �?nbsp; 06-5-Drop-out操作.ts
�?nbsp; �?nbsp; 07-6-权重初始化方法对�?ts
�?nbsp; �?nbsp; 08-7-初始化标准差对结果的影响.ts
�?nbsp; �?nbsp; 09-8-正则化对�l�果的媄(ji��ng)�?ts
�?nbsp; �?nbsp; 10-9-加蝲模型�q�行���试.ts
�?nbsp; �?/div>
�?nbsp; ├─06-案例实战��L(f��ng)�����经�|�络
�?nbsp; �?nbsp; 01-1-��L(f��ng)��层构�?ts
�?nbsp; �?nbsp; 02-1-��L(f��ng)��层构�?ts
�?nbsp; �?nbsp; 03-3-BatchNormalization效果.ts
�?nbsp; �?nbsp; 04-4-参数�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 05-5-�|�络���试效果.ts
�?nbsp; �?/div>
�?nbsp; └─07-案例实战LSTM旉���序列预测��d��
�?nbsp; 01-1-旉���序列模型.ts
�?nbsp; 02-2-�|�络�l�构与参数定�?ts
�?nbsp; 03-3-构徏LSTM模型.ts
�?nbsp; 04-4-训练模型与效果展�C?ts
�?nbsp; 05-5-多序列预���结�?ts
�?nbsp; 06-6-股票数据预测.ts
�?nbsp; 07-7-数据预处�?ts
�?nbsp; 08-8-预测�l�果展示.ts
�?/div>
├─10-选修�Q�Python数据分析案例实战
�?nbsp; ├─01-KIVA��h��数据
�?nbsp; �?nbsp; 01-kiva��h��数据集介�l?ts
�?nbsp; �?nbsp; 02-2-各个国家��h��需�?ts
�?nbsp; �?nbsp; 03-3-��h��金额与还?g��u)��N��隔分�?ts
�?nbsp; �?nbsp; 04-5-深入各个行业分析.ts
�?nbsp; �?nbsp; 05-6-针对旉���序列�q�行分析.ts
�?nbsp; �?nbsp; 06-7-各项数据指标�l�计分析.ts
�?nbsp; �?/div>
�?nbsp; ├─02-订单数据集分�?/div>
�?nbsp; �?nbsp; 01-8-预测�l�果展示.ts
�?nbsp; �?nbsp; 02-2-双变量热度图�l�制�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 03-3-复购情况�Ҏ(gu��)��分析.ts
�?nbsp; �?nbsp; 04-4-购物车情况与复购.ts
�?nbsp; �?nbsp; 05-5-聚类划分商品.ts
�?nbsp; �?/div>
�?nbsp; ├─03-��Z���l�计分析的电(sh��)影推�?/div>
�?nbsp; �?nbsp; 01-1-�?sh��)�?ji��ng)数据与环境配�|?ts
�?nbsp; �?nbsp; 02-2-数据与关键词信息展示.ts
�?nbsp; �?nbsp; 03-3-关键词云与直方图可视化展�C?ts
�?nbsp; �?nbsp; 04-4-�?sh��)�?ji��ng)特征数据可视�?ts
�?nbsp; �?nbsp; 05-5数据清洗�Ҏ(gu��)��分析.ts
�?nbsp; �?nbsp; 06-6-�~�失值填充方�?ts
�?nbsp; �?nbsp; 07-7-推荐引擎构�?ts
�?nbsp; �?nbsp; 08-8-数据特征构�?ts
�?nbsp; �?nbsp; 09-9-得出推荐�l�果.ts
�?nbsp; �?/div>
�?nbsp; ├─04-�U�约出租车徏�?/div>
�?nbsp; �?nbsp; 01-1-�U�约出租车运行情冉|��据概�q?ts
�?nbsp; �?nbsp; 02-2-聚类区域划分.ts
�?nbsp; �?nbsp; 03-3-客流���势动态展�C?ts
�?nbsp; �?nbsp; 04-4-区域��d��情况分析.ts
�?nbsp; �?nbsp; 05-5-用户数据特征分析.ts
�?nbsp; �?nbsp; 06-6-不同�c�d��的出�U��R�q�行情况�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 07-7-客户数据特征可视化分�?ts
�?nbsp; �?nbsp; 08-8-聚类特征信息可视化展�C?ts
�?nbsp; �?nbsp; 09-9-xgboost模型�q�行分析预测.ts
�?nbsp; �?nbsp; 10-10-加入天气特征对结果的影响分析.ts
�?nbsp; �?/div>
�?nbsp; ├─05-商品信息可视化与文本分析
�?nbsp; �?nbsp; 01-1-在线商城商品数据信息概述.ts
�?nbsp; �?nbsp; 02-2-商品�c�d��划分方式.ts
�?nbsp; �?nbsp; 03-3-商品�c�d��可视化展�C?ts
�?nbsp; �?nbsp; 04-4-商品描述长度对�h(hu��n)格的影响分析.ts
�?nbsp; �?nbsp; 05-5-关键词的词云可视化展�C?ts
�?nbsp; �?nbsp; 06-6-��Z��tf-idf提取关键词信�?ts
�?nbsp; �?nbsp; 07-7-通过降维�q�行可视化展�C?ts
�?nbsp; �?nbsp; 08-8-聚类分析与主题模型展�C?ts
�?nbsp; �?/div>
�?nbsp; └─06-数据分析-机器学习(f��n)模板
�?nbsp; 01-1-人口普查预测��d��概述.ts
�?nbsp; 02-2-单特征与�~�失值展�C?ts
�?nbsp; 03-3-人口普查数据集清�z?ts
�?nbsp; 04-4-人口信息数据特征工程展示.ts
�?nbsp; 05-5-单变量展�C?ts
�?nbsp; 06-6-双变量分�?ts
�?nbsp; 07-7-开发新变量.ts
�?nbsp; 08-8-ROC与AUC模型评估标准.ts
�?nbsp; 09-9-机器学习(f��n)���法模型效果�Ҏ(gu��)��.ts
�?/div>
├─11-选修�Q�机器学�?f��n)进阶实�?/div>
�?nbsp; ├─01-GBDT提升���法
�?nbsp; �?nbsp; 01-1-回归�?w��i)模�?ts
�?nbsp; �?nbsp; 02-2-Adaboost���法.ts
�?nbsp; �?nbsp; 03-3-GBDT工作���程.ts
�?nbsp; �?nbsp; 04-4-回归��d��.ts
�?nbsp; �?nbsp; 05-5-分类��d��.ts
�?nbsp; �?nbsp; 06-6-可视�?ts
�?nbsp; �?/div>
�?nbsp; ├─01-数据特征
�?nbsp; �?nbsp; 01-1-基本数值特�?ts
�?nbsp; �?nbsp; 02-2-常用特征构造手�D?ts
�?nbsp; �?nbsp; 03-3-旉���特征处理.ts
�?nbsp; �?nbsp; 04-4-文本特征处理.ts
�?nbsp; �?nbsp; 05-5-构造文本向�?ts
�?nbsp; �?nbsp; 06-6-词向量特�?ts
�?nbsp; �?nbsp; 07-7-计算机眼中的囑փ�.ts
�?nbsp; �?/div>
�?nbsp; ├─02-xgboost-gbdt-lightgbm提升���法框架�Ҏ(gu��)��
�?nbsp; �?nbsp; 01-1-GBDT效果.ts
�?nbsp; �?nbsp; 02-2-Xgboost效果.ts
�?nbsp; �?nbsp; 03-3-lightGBM效果.ts
�?nbsp; �?/div>
�?nbsp; ├─04-4.使用lightgbm�q�行饭店���量预测
�?nbsp; �?nbsp; 01-1-饭店���量数据介绍.ts
�?nbsp; �?nbsp; 02-2-数据汇�?ts
�?nbsp; �?nbsp; 03-3-��ȝ��点筛�?ts
�?nbsp; �?nbsp; 04-4-特征提取.ts
�?nbsp; �?nbsp; 05-5-lightgbm建模.ts
�?nbsp; �?/div>
�?nbsp; ├─05-人口普查数据集项目实�?收入预测
�?nbsp; �?nbsp; 01-1-人口普查预测��d��概述.ts
�?nbsp; �?nbsp; 02-2-单特征与�~�失值展�C?ts
�?nbsp; �?nbsp; 03-3-�W�一步:(x��)数据清洗.ts
�?nbsp; �?nbsp; 04-4-特征工程.ts
�?nbsp; �?nbsp; 05-5-单变量展�C?ts
�?nbsp; �?nbsp; 06-6-双变量分�?ts
�?nbsp; �?nbsp; 07-7-开发新变量.ts
�?nbsp; �?nbsp; 08-8-ROC与AUC模型评估标准.ts
�?nbsp; �?nbsp; 09-9-机器学习(f��n)模型.ts
�?nbsp; �?/div>
�?nbsp; ├─05-降维���法-�U�性判别分�?/div>
�?nbsp; �?nbsp; 01-1-�U�性判别分析要解决的问�?ts
�?nbsp; �?nbsp; 02-2-�U�性判别分析要优化的目�?ts
�?nbsp; �?nbsp; 03-3-�U�性判别分析求�?ts
�?nbsp; �?nbsp; 04-4-实现�U�性判别分析进行降�l��Q�?ts
�?nbsp; �?nbsp; 05-5-求解得出降维�l�果.ts
�?nbsp; �?/div>
�?nbsp; ├─07-贝叶斯优化及(qi��ng)其工具包使用
�?nbsp; �?nbsp; 01-1-贝叶斯优化概�q?ts
�?nbsp; �?nbsp; 02-2-工具包��用方�?ts
�?nbsp; �?nbsp; 03-3-贝叶斯优化效�?ts
�?nbsp; �?nbsp; 04-4-调整参数�I�间.ts
�?nbsp; �?/div>
�?nbsp; ├─08-贝叶斯优化实�?/div>
�?nbsp; �?nbsp; 01-1-基础模型建立.ts
�?nbsp; �?nbsp; 02-2-讄���参数�I�间.ts
�?nbsp; �?nbsp; 03-3-随机优化�l�果.ts
�?nbsp; �?nbsp; 04-4-贝叶斯优化效�?ts
�?nbsp; �?nbsp; 05-5-�Ҏ(gu��)���Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 06-6-参数变化情况.ts
�?nbsp; �?/div>
�?nbsp; ├─09-EM���法
�?nbsp; �?nbsp; 01-1-EM���法要解决的问题.ts
�?nbsp; �?nbsp; 02-2-隐变量问�?ts
�?nbsp; �?nbsp; 03-3-EM���法求解实例.ts
�?nbsp; �?nbsp; 04-4-Jensen不等�?ts
�?nbsp; �?nbsp; 05-5-GMM模型.ts
�?nbsp; �?nbsp; 06-6-GMM实例.ts
�?nbsp; �?nbsp; 07-7-GMM聚类.ts
�?nbsp; �?/div>
�?nbsp; ├─10-HMM隐马��?d��ng)科夫模�?/div>
�?nbsp; �?nbsp; 01-1-马尔�U�夫模型.ts
�?nbsp; �?nbsp; 02-2-隐马��?d��ng)科夫模型基本出发�?ts
�?nbsp; �?nbsp; 03-3-�l�成与要解决的问�?ts
�?nbsp; �?nbsp; 04-4-暴力求解�Ҏ(gu��)��.ts
�?nbsp; �?nbsp; 05-5-复杂度计��?ts
�?nbsp; �?nbsp; 06-6-前向���法.ts
�?nbsp; �?nbsp; 07-7-前向���法求解实例.ts
�?nbsp; �?nbsp; 08-8-Baum-Welch���法.ts
�?nbsp; �?nbsp; 09-9-参数求解.ts
�?nbsp; �?nbsp; 10-10-�l�特比算�?ts
�?nbsp; �?/div>
�?nbsp; ├─11-HMM案例实战
�?nbsp; �?nbsp; 01-1-hmmlearn工具�?ts
�?nbsp; �?nbsp; 02-2-工具包��用方�?ts
�?nbsp; �?nbsp; 03-3-中文分词��d��.ts
�?nbsp; �?nbsp; 04-4-实现中文分词.ts
�?nbsp; �?/div>
�?nbsp; ├─12-推荐�pȝ��
�?nbsp; �?nbsp; 01-0-开�?ts
�?nbsp; �?nbsp; 02-1-推荐�pȝ��应用.ts
�?nbsp; �?nbsp; 03-2-推荐�pȝ��要完成的��d��.ts
�?nbsp; �?nbsp; 04-3-�怼�度计��?ts
�?nbsp; �?nbsp; 05-4-��Z��用户的协同过�?ts
�?nbsp; �?nbsp; 06-5-��Z��物品的协同过�?ts
�?nbsp; �?nbsp; 07-6-隐语义模�?ts
�?nbsp; �?nbsp; 08-7-隐语义模型求�?ts
�?nbsp; �?nbsp; 09-8-模型评估标准.ts
�?nbsp; �?/div>
�?nbsp; ├─13-��Z���l�计分析的电(sh��)影推�?/div>
�?nbsp; �?nbsp; 01-1-数据与环境配�|?ts
�?nbsp; �?nbsp; 02-2-数据与关键词信息展示.ts
�?nbsp; �?nbsp; 03-3-关键词云与直方图展示.ts
�?nbsp; �?nbsp; 04-4-特征可视�?ts
�?nbsp; �?nbsp; 05-5-数据清洗概述.ts
�?nbsp; �?nbsp; 06-6-�~�失值填充方�?ts
�?nbsp; �?nbsp; 07-7-推荐引擎构�?ts
�?nbsp; �?nbsp; 08-8-数据特征构�?ts
�?nbsp; �?nbsp; 09-9-得出推荐�l�果.ts
�?nbsp; �?/div>
�?nbsp; ├─13-音乐推荐�pȝ��实战
�?nbsp; �?nbsp; 01-1-音乐推荐��d��概述.ts
�?nbsp; �?nbsp; 02-2-数据集整�?ts
�?nbsp; �?nbsp; 03-3-��Z��物品的协同过�?ts
�?nbsp; �?nbsp; 04-4-物品�怼�度计���与推荐.ts
�?nbsp; �?nbsp; 05-5-SVD矩阵分解.ts
�?nbsp; �?nbsp; 06-6-��Z��矩阵分解的音乐推�?ts
�?nbsp; �?/div>
�?nbsp; ├─15-NLP-文本特征�Ҏ(gu��)���Ҏ(gu��)��
�?nbsp; �?nbsp; 01-1.1-��d��概述.ts
�?nbsp; �?nbsp; 02-2-词袋模型.ts
�?nbsp; �?nbsp; 03-3-词袋模型分析.mp4
�?nbsp; �?nbsp; 04-4-TFIDF模型.ts
�?nbsp; �?nbsp; 05-5-word2vec词向量模�?ts
�?nbsp; �?nbsp; 06-6-深度学习(f��n)模型.ts
�?nbsp; �?/div>
�?nbsp; ├─15-学习(f��n)曲线
�?nbsp; �?nbsp; 01-1-Bian与Variance曲线.ts
�?nbsp; �?nbsp; 02-2-数据集中的结�?ts
�?nbsp; �?nbsp; 03-3-曲线实验�l�果.ts
�?nbsp; �?/div>
�?nbsp; ├─17-使用word2vec分类��d��
�?nbsp; �?nbsp; 01-1-��p��情感分类.ts
�?nbsp; �?nbsp; 02-2-��Z��词袋模型训练分类�?ts
�?nbsp; �?nbsp; 03-3-准备word2vec输入数据.ts
�?nbsp; �?nbsp; 04-4-使用gensim构徏word2vec词向量(斎ͼ�(j��).ts
�?nbsp; �?/div>
�?nbsp; ├─18-Tensorflow自己打造word2vec
�?nbsp; �?nbsp; 01-1-数据与�Q务流�E?ts
�?nbsp; �?nbsp; 02-2-数据清洗.ts
�?nbsp; �?nbsp; 03-3-batch数据制作.mp4
�?nbsp; �?nbsp; 04-4-�|�络训练.ts
�?nbsp; �?nbsp; 05-5-可视化展�C?ts
�?nbsp; �?/div>
�?nbsp; ├─19-制作自己常用工具�?/div>
�?nbsp; �?nbsp; 01-1-��Z��么要做自��q��数据工具�?ts
�?nbsp; �?nbsp; 02-2-工具包注�?ts
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