高端实战 Python数据分析与机器学习实战 Numpy/Pandas/Matplotlib等常用库精讲
===============课程目录===============│├<01-Python科学计算库-Numpy>
││├课时01.课程介绍（主题与大纲）.flv
││├课时02.机器学习概述.flv
││├课时03.使用Anaconda安装python环境.flv
││├课时04.课程数据，代码，PPT（在参考资料界面）.swf
││├课时05.科学计算库Numpy.flv
││├课时06.Numpy基础结构.flv
││├课时07.Numpy矩阵基础.flv_d.flv
││├课时08.Numpy常用函数.flv_d.flv
││├课时09.矩阵常用操作.flv_d.flv
││└课时10.不同复制操作对比.flv_d.flv
│├<02-python数据分析处理库-Pandas>
││├课时11.Pandas数据读取.flv
││├课时12.Pandas索引与计算.flv_d.flv
││├课时13.Pandas数据预处理实例.flv_d.flv
││├课时14.Pandas常用预处理方法.flv_d.flv
││├课时15.Pandas自定义函数.flv_d.flv
││└课时16.Series结构.flv_d.flv
│├<03-Python数据可视化库-Matplotlib>
││├课时17.折线图绘制.flv
││├课时18.子图操作.flv_d.flv
││├课时19.条形图与散点图.flv_d.flv
││├课时20.柱形图与盒图.flv_d.flv
││└课时21.细节设置.flv_d.flv
│├<04-Python可视化库Seaborn>
││├课时22.Seaborn简介.flv
││├课时23.整体布局风格设置.flv_d.flv
││├课时24.风格细节设置.flv_d.flv
││├课时25.调色板.flv_d.flv
││├课时26.调色板.flv_d.flv
││├课时27.调色板颜色设置.flv_d.flv
││├课时28.单变量分析绘图.flv_d.flv
││├课时29.回归分析绘图.flv_d.flv
││├课时30.多变量分析绘图.flv_d.flv
││├课时31.分类属性绘图.flv_d.flv
││├课时32.Facetgrid使用方法.flv_d.flv
││└课时33.Facetgrid绘制多变量.flv_d.flv
│├<05-回归算法>
││├课时34.热度图绘制.flv_d.flv
││├课时35.回归算法综述.flv_d.flv
││├课时36.回归误差原理推导.flv_d.flv
││├课时37.回归算法如何得出最优解.flv_d.flv
││├课时38.基于公式推导完成简易线性回归.flv_d.flv
││└课时39.逻辑回归与梯度下降.flv_d.flv
│├<06-决策树>
││├课时40.使用梯度下降求解回归问题.flv_d.flv
││├课时41.决策树算法综述.flv_d.flv
││├课时42.决策树熵原理.flv_d.flv
││├课时43.决策树构造实例.flv_d.flv
││├课时44.信息增益原理.flv_d.flv
││├课时45.信息增益率的作用.flv_d.flv
││├课时46.决策树剪枝策略.flv_d.flv
││└课时47.随机森林模型.flv_d.flv
│├<07-贝叶斯算法>
││├课时48.决策树参数详解.flv_d.flv
││├课时49.贝叶斯算法概述.flv_d.flv
││├课时50.贝叶斯推导实例.flv_d.flv
││├课时51.贝叶斯拼写纠错实例.flv_d.flv
││└课时52.垃圾邮件过滤实例.flv_d.flv
│├<08-支持向量机>
││├课时53.贝叶斯实现拼写检查器.flv_d.flv
││├课时54.支持向量机要解决的问题.flv_d.flv
││├课时55.支持向量机目标函数.flv_d.flv
││├课时56.支持向量机目标函数求解.flv_d.flv
││├课时57.支持向量机求解实例.flv_d.flv
││├课时58.支持向量机软间隔问题.flv_d.flv
││└课时59.支持向量核变换.flv_d.flv
│├<09-神经网络>
││├课时60.SMO算法求解支持向量机.flv_d.flv
││├课时61.初识神经网络.flv_d.flv
││├课时62.计算机视觉所面临的挑战.flv_d.flv
││├课时63.K近邻尝试图像分类.flv_d.flv
││├课时64.超参数的作用.flv_d.flv
││├课时65.线性分类原理.flv_d.flv
││├课时66.神经网络-损失函数.flv_d.flv
││├课时67.神经网络-正则化惩罚项.flv_d.flv
││├课时68.神经网络-softmax分类器.flv_d.flv
││├课时69.神经网络-最优化形象解读.flv_d.flv
││├课时70.神经网络-梯度下降细节问题.flv_d.flv
││├课时71.神经网络-反向传播.flv_d.flv
││├课时72.神经网络架构.flv_d.flv
││├课时73.神经网络实例演示.flv_d.flv
││└课时74.神经网络过拟合解决方案.flv_d.flv
│├<10-Xgboost集成算法>
││├课时75.感受神经网络的强大.flv_d.flv
││├课时76.集成算法思想.flv_d.flv
││├课时77.xgboost基本原理.flv_d.flv
││├课时78.xgboost目标函数推导.flv_d.flv
││├课时79.xgboost求解实例.flv_d.flv
││├课时80.xgboost安装.flv_d.flv
││└课时81.xgboost实战演示.flv_d.flv
│├<11-自然语言处理词向量模型-Word2Vec>
││├课时82.Adaboost算法概述.flv_d.flv
││├课时83.自然语言处理与深度学习加微信ff1318860.flv_d.flv
││├课时84.语言模型.flv_d.flv
││├课时85.-N-gram模型.flv_d.flv
││├课时86.词向量.flv_d.flv
││├课时87.神经网络模型.flv_d.flv
││├课时88.Hierarchical.Softmax.flv_d.flv
││├课时89.CBOW模型实例.flv_d.flv
││├课时90.CBOW求解目标.flv_d.flv
││└课时91.梯度上升求解.flv_d.flv
│├<12-K近邻与聚类>
││├课时92.负采样模型.flv_d.flv
││├课时93.无监督聚类问题.flv_d.flv
││├课时94.聚类结果与离群点分析.flv_d.flv
││├课时95.K-means聚类案例对NBA球员进行评估.flv_d.flv
││├课时96.使用Kmeans进行图像压缩.flv_d.flv
││└课时97.K近邻算法原理.flv_d.flv
│├<13-PCA降维与SVD矩阵分解>
││├课时100.PCA实例.flv_d.flv
││├课时101.SVD奇异值分解原理.flv_d.flv
││├课时98.K近邻算法代码实现.flv_d.flv
││└课时99.PCA基本原理.flv_d.flv
│├<14-scikit-learn模型建立与评估>
││├课时102.SVD推荐系统应用实例.flv_d.flv
││├课时103.使用python库分析汽车油耗效率.flv
││├课时104.使用scikit-learn库建立回归模型.flv_d.flv
││├课时105.使用逻辑回归改进模型效果.flv_d.flv
││├课时106..模型效果衡量标准.flv_d.flv
││├课时107.ROC指标与测试集的价值.flv_d.flv
││└课时108.交叉验证.flv_d.flv
│├<15-Python库分析科比生涯数据>
││├课时109.多类别问题.flv_d.flv
││├课时110.Kobe.Bryan生涯数据读取与简介.flv
││├课时111.特征数据可视化展示.flv_d.flv
││└课时112.数据预处理.flv_d.flv
│├<16-机器学习项目实战-泰坦尼克获救预测>
││├课时113.使用Scikit-learn建立模型.flv_d.flv
││├课时114.船员数据分析.flv
││├课时115.数据预处理.flv_d.flv
││├课时116.使用回归算法进行预测.flv_d.flv
││└课时117.使用随机森林改进模型.flv_d.flv
│├<17-机器学习项目实战-交易数据异常检测>
││├课时118.随机森林特征重要性分析.flv_d.flv
││├课时119.案例背景和目标.flv_d.flv
││├课时120.样本不均衡解决方案.flv_d.flv
││├课时121.下采样策略.flv_d.flv
││├课时122.交叉验证.flv_d.flv
││├课时123.模型评估方法.flv_d.flv
││├课时124.正则化惩罚.flv_d.flv
││├课时125.逻辑回归模型.flv_d.flv
││├课时126.混淆矩阵.flv_d.flv
││└课时127.逻辑回归阈值对结果的影响.flv_d.flv
│├<18-Python文本数据分析：新闻分类任务>
││├课时128.SMOTE样本生成策略.flv_d.flv
││├课时129.文本分析与关键词提取.flv_d.flv
││├课时130.相似度计算.flv_d.flv
││├课时131.新闻数据与任务简介.flv_d.flv
││├课时132.TF-IDF关键词提取.flv_d.flv
││└课时133.LDA建模.flv_d.flv
│├<19-Python时间序列分析>
││├课时134.基于贝叶斯算法进行新闻分类.flv_d.flv
││├课时135.章节简介.flv
││├课时136.Pandas生成时间序列.flv_d.flv
││├课时137.Pandas数据重采样.flv_d.flv
││├课时138.Pandas滑动窗口.flv_d.flv
││├课时139.数据平稳性与差分法.flv_d.flv
││├课时140.ARIMA模型.flv_d.flv
││├课时141.相关函数评估方法.flv_d.flv
││├课时142.建立ARIMA模型.flv_d.flv
││├课时143.参数选择.flv_d.flv
││├课时144.股票预测案例.flv_d.flv
││└课时145.使用tsfresh库进行分类任务.flv_d.flv
│├<20-使用Gensim库构造中文维基百度数据词向量模型>
││├课时146.维基百科词条EDA.flv_d.flv
││├课时147.使用Gensim库构造词向量.flv_d.flv
││├课时148.维基百科中文数据处理.flv_d.flv
││└课时149.Gensim构造word2vec模型.flv_d.flv
│├<21-机器学习项目实战-贷款申请最大化利润>
││├课时150.测试模型相似度结果.flv_d.flv
││├课时151.数据清洗过滤无用特征.flv_d.flv
││├课时152.数据预处理.flv_d.flv
││└课时153.获得最大利润的条件与做法.flv_d.flv
│├<22-机器学习项目实战-用户流失预警>
││├课时154.预测结果并解决样本不均衡问题.flv_d.flv
││├课时155.数据背景介绍.flv_d.flv
││├课时156.数据预处理.flv_d.flv
││├课时157.尝试多种分类器效果.flv_d.flv
││└课时158.结果衡量指标的意义.flv_d.flv
│├<23-探索性数据分析-足球赛事数据集>
││├课时159.应用阈值得出结果.flv_d.flv
││├课时160.内容简介.flv_d.flv
││├课时161.数据背景介绍.flv
││├课时162.数据读取与预处理.flv_d.flv
││├课时163.数据切分模块.flv_d.flv
││├课时164.缺失值可视化分析.flv_d.flv
││├课时165.特征可视化展示.flv_d.flv
││├课时166.多特征之间关系分析.flv_d.flv
││└课时167.报表可视化分析.flv_d.flv
│├<24-探索性数据分析-农粮组织数据集>
││├课时168.红牌和肤色的关系.flv_d.flv
││├课时169.数据背景简介.flv_d.flv
││├课时170.数据切片分析.flv_d.flv
││├课时171.单变量分析.flv_d.flv
││├课时172.峰度与偏度.flv_d.flv
││├课时173.数据对数变换.flv_d.flv
││└课时174.数据分析维度.flv_d.flv
│├<25-机器学习项目实战-HTTP日志聚类分析>
││├课时175.变量关系可视化展示.flv_d.flv
││├课时176.建立特征工程.flv_d.flv
││├课时177.特征数据预处理.flv_d.flv
││└课时178.应用聚类算法得出异常IP点.flv_d.flv
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│├<25-机器学习项目实战-HTTP日志聚类分析>
││├课时175.变量关系可视化展示.flv_d.flv
││├课时176.建立特征工程.flv_d.flv
││├课时177.特征数据预处理.flv_d.flv
││└课时178.应用聚类算法得出异常IP点.flv_d.flv
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