├── .gitignore ├── D01_sklearn用法详解 ├── demo │ ├── 生成一个随机N类分类问题.py │ ├── 生成两个扣在一起的月形半圆.py │ ├── 生成同心圆分布.py │ ├── 生成线性分布随机数据.py │ ├── 生成随机各向同性正态分布斑点.py │ └── 鸢尾花数据.py ├── kd树（KDTree）.md ├── make_blobs_图1.png ├── make_blobs_图2.png ├── 生成各向同性的斑点状数据（make_blobs）.md ├── 距离计算(DistanceMetric).md └── 随机数据生成(临时).md ├── E01_LPL比赛结果预测 ├── 1_赛事列表采集.py ├── 2_赛事比赛采集.py ├── 3_比赛场次采集.py ├── LPL比赛数据爬虫.md ├── README.md ├── spider_1.py ├── spider_2.py ├── spider_3.py └── 赛事信息对照表.md ├── R01_《统计学习方法》啃书辅助 ├── README.md ├── 第2章_感知机 │ ├── README.md │ ├── 感知机学习算法的原始形式.py │ └── 感知机学习算法的对偶形式.py ├── 第3章_k近邻法 │ ├── README.md │ ├── S折交叉验证计算k最优的KNN分类器.py │ ├── 基于kd树实现的k近邻计算.py │ ├── 简单交叉验证计算k最优的KNN分类器.py │ └── 线性扫描实现的k近邻计算.py ├── 第4章_朴素贝叶斯法 │ ├── README.md │ ├── 支持连续型特征的朴素贝叶斯(sklearn实现).py │ ├── 朴素贝叶斯算法.py │ └── 贝叶斯估计.py ├── 第5章_决策树 │ ├── C4.5的生成算法(不包括剪枝).py │ ├── CART分类树-测试实例1(sklearn实现).py │ ├── CART分类树-测试实例2(sklearn实现).py │ ├── CART回归树-测试实例(sklearn实现).py │ ├── ID3算法生成决策树(不包含剪枝).py │ ├── ID3算法生成决策树(包含剪枝).py │ ├── README.md │ ├── 例5.1数据集.py │ ├── 信息增益.py │ ├── 信息增益比.py │ ├── 条件熵.py │ └── 熵.py ├── 第6章_逻辑斯蒂回归和最大熵模型 │ ├── README.md │ ├── 拟牛顿法.py │ └── 改进的迭代尺度法IIS.py ├── 第7章_支持向量机 │ ├── README.md │ ├── SMO实现的支持向量机(原生Python).py │ ├── 字符串核函数的动态规划计算.py │ ├── 支持向量机关系图.png │ ├── 线性支持向量机(sklearn).py │ └── 非线性支持向量机(sklearn).py ├── 第8章_提升算法 │ ├── AdaBoost算法实现(sklearn+乳腺癌).py │ ├── AdaBoost算法实现(原生Python+乳腺癌).py │ ├── AdaBoost算法实现(原生Python+例8.1).py │ ├── README.md │ ├── 回归问题的提升树算法(sklearn+波士顿房价).py │ ├── 回归问题的提升树算法(原生Python+例8.2).py │ └── 回归问题的提升树算法(原生Python+波士顿房价).py ├── 附录A_梯度下降法 │ ├── README.md │ ├── 基于黄金分割法的一维搜索.py │ ├── 最速下降法.py │ ├── 梯度下降法.py │ └── 梯度向量计算.py ├── 附录B_牛顿法和拟牛顿法 │ ├── BFGS算法(Sherman-Morrison公式).py │ ├── BFGS算法.py │ ├── Broyden算法.py │ ├── DFP算法.py │ ├── README.md │ ├── 牛顿法.py │ ├── 计算逆矩阵.py │ └── 计算黑塞矩阵.py └── 附录C_拉格朗日对偶性 │ └── README.md ├── README.md ├── code ├── __init__.py ├── adaboost │ ├── __init__.py │ ├── _adaboost.py │ └── _adaboost_regressor.py ├── basic │ ├── __init__.py │ └── binary_tree_node.py ├── dicision_tree │ ├── __init__.py │ ├── _conditional_extropy.py │ ├── _decision_tree_c45_without_pruning.py │ ├── _decision_tree_id3.py │ ├── _decision_tree_id3_without_pruning.py │ ├── _entropy.py │ ├── _information_gain.py │ └── _information_gain_ratio.py ├── example │ ├── __init__.py │ └── _li.py ├── gradient_descent │ ├── __init__.py │ ├── _golden_section_for_line_search.py │ ├── _gradient_descent.py │ ├── _partial_derivative.py │ └── _steepest_descent.py ├── knn │ ├── __init__.py │ ├── _build_best_knn_s_fold_cross_validation.py │ ├── _build_best_knn_simple_cross_validation.py │ ├── _eucliean_distance.py │ ├── _kd_tree.py │ ├── _kd_tree_knn.py │ ├── _linear_sweep_knn.py │ ├── _lp_distance.py │ └── _manhattan_distance.py ├── maximum_entropy_model │ ├── __init__.py │ ├── _bfgs_algorithm_for_maximum_entropy_model.py │ ├── _improved_iterative_scaling.py │ └── _newton_method_linear.py ├── naive_bayes │ ├── __init__.py │ ├── _naive_bayes_algorithm_array.py │ ├── _naive_bayes_algorithm_hashmap.py │ └── _naive_bayes_algorithm_with_smoothing.py ├── newton_method │ ├── __init__.py │ ├── _bfgs_algorithm.py │ ├── _bfgs_algorithm_with_sherman_morrison.py │ ├── _broyden_algorithm.py │ ├── _dfp_algorithm.py │ ├── _get_hessian.py │ └── _newton_method.py ├── perceptron │ ├── __init__.py │ ├── _dual_form.py │ ├── _gram.py │ └── _original_form.py └── svm │ ├── __init__.py │ └── _svm.py ├── 配套教程 ├── NLP教程 │ ├── Day01_学习大纲及入门资源整理.md │ ├── Day02_中文词典分词的切分算法.md │ ├── Day03_字典树.md │ ├── Day04_HanLP词典分词的Python实现.md │ ├── Day05_AC自动机.md │ ├── Day06_首字散列其余二分的字典树.md │ ├── Day07_基于HanLP实现的停用词过滤.md │ ├── Day08_基于HanLP实现的中文文本清洗.md │ ├── Day09_基于HanLP实现的拼音转换.md │ ├── Day10_文章关键词提取：词频统计.md │ ├── Day11_文章关键词提取：TF-IDF.md │ ├── Day12_文章关键词提取：TextRank.md │ ├── Day13_语言模型.md │ ├── Day14_语言模型的训练与预测.md │ ├── Day15_序列标注.md │ ├── Day16_隐马尔可夫模型.md │ ├── Day17_中文分词准确率评测方法.md │ ├── Day18_基于HanLP的隐马尔可夫模型实现.md │ ├── Day19_NLP领域的分类问题.md │ ├── Day20_基于信息熵和互信息的新词提取实现.md │ ├── Day21_短语提取.md │ ├── Day22_spaCy实现的英文词形还原.md │ └── Day22_英文词形还原（各处理库准确率评测）.md ├── Python入门与Python爬虫 │ ├── README.md │ ├── Week-00 │ │ ├── Day-001 │ │ │ ├── Windows的Path环境变量.md │ │ │ └── 第001天教程.md │ │ ├── Day-002 │ │ │ ├── Exercise01-Hello World.md │ │ │ ├── Exercise01 │ │ │ │ ├── Hello World.ipynb │ │ │ │ ├── Hello World.py │ │ │ │ └── 输入输出用户名.py │ │ │ ├── Jupyter Nbextensions插件配置方法.md │ │ │ └── 数据挖掘领域建议安装的包.md │ │ └── 第00周_环境配置.md │ ├── Week-01 │ │ ├── Day-003 │ │ │ ├── ==和is的区别(选学).ipynb │ │ │ ├── Exercise02-语言元素.md │ │ │ ├── 常用变量类型与基础运算.ipynb │ │ │ └── 第003天教程.md │ │ ├── Day-004 │ │ │ └── Exercise03-程序运行流程.md │ │ ├── Day-005 │ │ │ └── Exercise04-常用数据类型操作.md │ │ ├── Day-006 │ │ │ └── Exercise05-函数.md │ │ ├── Day-007 │ │ │ ├── Exercise08-Python常用内置函数和模块.md │ │ │ ├── Python内置函数详细说明.md │ │ │ └── 第007天教程.md │ │ ├── Example-0102 │ │ │ └── 案例0102讲解_登录问题.md │ │ ├── Example-0103 │ │ │ └── 案例0103讲解_牛吃草问题.md │ │ ├── Python初学注意事项.md │ │ └── 第01周_Python语言基础(一).md │ ├── Week-02 │ │ ├── Day-008 │ │ │ ├── Exercise06-正则表达式.md │ │ │ └── 第008天教程.md │ │ ├── Day-009 │ │ │ └── Exercise07-数据存储.md │ │ ├── Day-010 │ │ │ └── Exercise09-线程和进程.md │ │ ├── Day-011 │ │ │ ├── Exercise10-Excel文档读写.md │ │ │ └── 第011天教程.md │ │ ├── Day-012 │ │ │ └── 第012天教程.md │ │ ├── Example-0201 │ │ │ ├── 全唐诗.txt │ │ │ ├── 全唐诗文本整理_解法1.py │ │ │ ├── 全唐诗文本整理_解法2.py │ │ │ └── 案例0201讲解_全唐诗文本整理.md │ │ ├── Example-0202 │ │ │ ├── 案例0202讲解_直播弹幕数据清洗.md │ │ │ ├── 直播间弹幕数据清洗.py │ │ │ └── 聆听丶芒果鱼直播间时间切片弹幕.json │ │ └── 第02周_Python语言基础(二).md │ ├── Week-03 │ │ ├── Day-013 │ │ │ └── Exercise11-面对对象编程基础.md │ │ ├── Day-014 │ │ │ └── Exercise12-面对对象编程进阶.md │ │ ├── Day-015 │ │ │ └── Exercise13-访问网络资源.md │ │ ├── Day-016 │ │ │ └── Exercise14-SQL语言基础.md │ │ ├── Day-017 │ │ │ └── Exercise15-Mysql应用.md │ │ ├── Example-0301 │ │ │ ├── 全唐诗.json │ │ │ ├── 平水韵表.txt │ │ │ ├── 案例0301讲解_近体诗格律分析.md │ │ │ ├── 诗词格律讲解(参考).md │ │ │ ├── 近体诗格律分析(面对对象).py │ │ │ └── 近体诗格律分析(面对过程).py │ │ ├── Example-0302 │ │ │ ├── 中国地名表.json │ │ │ ├── 地名查询工具.py │ │ │ └── 案例0302讲解_地名查询工具.md │ │ ├── Example-0303 │ │ │ ├── 24点游戏算法_解法1.py │ │ │ ├── 24点游戏算法_解法2.py │ │ │ └── 案例0303讲解_24点游戏算法.md │ │ └── 第03周_Python语言基础(三).md │ ├── Week-04 │ │ ├── Example-0401 │ │ │ ├── image-20200525113720302.png │ │ │ ├── image-20200525114041712.png │ │ │ ├── image-20200525114226622.png │ │ │ ├── image-20200525115713853.png │ │ │ ├── image-20200525115941925.png │ │ │ ├── image-20200525120022795.png │ │ │ ├── image-20200525120129893.png │ │ │ ├── image-20200525120335623.png │ │ │ ├── image-20200525121204580.png │ │ │ ├── image-20200525121617879.png │ │ │ ├── 微博热搜榜采集.py │ │ │ └── 案例0401讲解_微博热搜榜采集.md │ │ ├── Example-0402 │ │ │ ├── image-20200526104936496.png │ │ │ ├── image-20200526122240902.png │ │ │ ├── 案例0402讲解_猫眼网播热度采集.md │ │ │ └── 猫眼网播热度采集.py │ │ ├── Example-0403 │ │ │ ├── image-20200528074918096.png │ │ │ ├── image-20200528094411449.png │ │ │ ├── 案例0403讲解_豆瓣TOP250电影采集.md │ │ │ ├── 豆瓣TOP250电影.json │ │ │ └── 豆瓣TOP250电影采集.py │ │ ├── Example-0404 │ │ │ ├── B站UP主发布视频信息采集.py │ │ │ ├── image-20200529100331604.png │ │ │ ├── image-20200529101539308.png │ │ │ ├── image-20200529105143860.png │ │ │ └── 案例0404讲解_B站UP主发布视频信息采集.md │ │ └── 第04周_Python爬虫基础(一).md │ ├── Week-05 │ │ └── 第05周_Python爬虫基础(二).md │ ├── Week-06 │ │ └── Python统计分析参考资料.md │ ├── face_detect.ipynb │ ├── face_merge.ipynb │ ├── 数据挖掘领域Github项目推荐.md │ ├── 数据挖掘领域技能树.md │ └── 豆瓣TOP250电影.json ├── 梯度下降法 │ ├── 01_梯度计算(原生代码实现).py │ ├── 02_梯度计算(scipy实现).py │ ├── 03_基于黄金分割法的一维搜索.py │ ├── 04_梯度下降法(原生代码实现).py │ ├── 05_梯度下降法(scipy计算梯度).py │ ├── 06_最速下降法(scipy计算梯度).py │ └── README.md ├── 统计学的Python实现(Jupyter) │ ├── 001_描述统计-总计(Sum).ipynb │ ├── 002_描述统计-均值.ipynb │ ├── 003_描述统计-中位数(Median).ipynb │ ├── 004_描述统计_众数.ipynb │ ├── 005_描述统计-最大值,最小值,极差(Maximum,Minimum,range).ipynb │ ├── 006_描述统计-方差(variance).ipynb │ ├── 007_描述统计-标准差(standard deviation).ipynb │ ├── 008_描述统计-标准误差(standard err,SE).ipynb │ ├── 009_描述统计-四分位数(quartile).ipynb │ ├── 010_描述统计-四分位距(interquartile range).ipynb │ ├── 011_描述统计-偏度(skewness).ipynb │ ├── 012_描述统计-峰度(kurtosis).ipynb │ ├── 013_描述统计-频数分布表(frequency table).ipynb │ ├── 014_几何平均数(geometric mean).ipynb │ ├── 015_调和平均数(harmonic mean).ipynb │ ├── 016_变异系数(coefficient of variation).ipynb │ ├── 联合分析(Conjoint Analysis)-checkpoint.ipynb │ └── 联合分析(Conjoint Analysis).ipynb └── 统计学的Python实现 │ ├── 001_描述统计_总计.md │ ├── 002_描述统计_均值.md │ ├── 003_描述统计_中位数.md │ ├── 004_描述统计_众数.md │ ├── 005_描述统计_最大值、最小值、极差.md │ ├── 006_描述统计_方差.md │ ├── 007_描述统计_标准差.md │ ├── 008_描述统计_标准误差.md │ ├── 009_描述统计_四分位数.md │ ├── 010_描述统计_四分位距.md │ ├── 011_描述统计_偏度.md │ ├── 012_描述统计_峰度.md │ ├── 013_描述统计_频数分布表.docx │ ├── 013_描述统计_频数分布表.md │ ├── 014_描述统计_几何平均数.md │ ├── 015_描述统计_调和平均数.md │ ├── 016_描述统计_变异系数.md │ ├── 017_标准正态分布概率计算.docx │ ├── 017_标准正态分布概率计算.md │ ├── 018_二项随机变量的概率计算.md │ ├── 019_任意正态分布计算概率.md │ └── 020_已知样本比例的抽样分布.md └── 面经收藏 └── 算法工程师 └── 阿里巴巴-算法工程师-2021.md /.gitignore: -------------------------------------------------------------------------------- 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