Python數(shù)據(jù)分析與機器學習

前言 \n
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第1章Python與數(shù)據(jù)分析 \n
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11概述 \n
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111引例 \n
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112數(shù)據(jù)分析與數(shù)據(jù)挖掘 \n
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12Python簡介 \n
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121Python特點 \n
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122Python應用場合 \n
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13數(shù)據(jù)分析流程 \n
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131明確目標 \n
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132獲取數(shù)據(jù) \n
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133清洗數(shù)據(jù) \n
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134特征工程 \n
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135構(gòu)建模型 \n
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136模型評估 \n
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14數(shù)據(jù)分析庫 \n
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141NumPy \n
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142Matplotlib \n
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143Pandas \n
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144Seaborn \n
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145Scipy \n
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146Sklearn \n
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15Python解釋器 \n
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151Ubuntu下安裝Python \n
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152Windows下安裝Python \n
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16Python編輯器 \n
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161IDLE \n
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162VScode \n
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163PyCharm \n
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164Anaconda \n
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165Jupyter \n
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17習題 \n
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第2章NumPy——數(shù)據(jù)分析基礎 \n
工具 \n
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21安裝NumPy \n
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22ndarray對象 \n
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221認識ndarray對象 \n
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222ndarray對象屬性 \n
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23創(chuàng)建ndarray對象 \n
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231zeros \n
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232ones \n
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233diag \n
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234arange \n
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235linspace \n
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236logspace \n
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24數(shù)組變換 \n
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241維度變換 \n
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242數(shù)組拼接 \n
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243數(shù)組分割 \n
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244數(shù)組復制 \n
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25索引和切片 \n
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26線性代數(shù) \n
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261矩陣運算 \n
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262矩陣轉(zhuǎn)置 \n
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263特征根和特征向量 \n
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27統(tǒng)計量 \n
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271平均值 \n
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272最值 \n
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273中位數(shù) \n
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274極差 \n
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275方差 \n
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276協(xié)方差 \n
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277皮爾森相關系數(shù) \n
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28習題 \n
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第3章Matplotlib——數(shù)據(jù)可視化 \n
工具 \n
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31安裝Matplotlib \n
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32繪圖步驟 \n
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321創(chuàng)建畫布 \n
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322繪圖函數(shù) \n
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323繪圖屬性 \n
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33子圖基本操作 \n
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331pltsubplot \n
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332figureadd_subplot \n
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333pltsubplots \n
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34繪圖 \n
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341折線圖 \n
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342氣泡圖 \n
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343餅圖 \n
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344直方圖 \n
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345條形圖 \n
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35概率分布 \n
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351泊松分布 \n
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352正態(tài)分布 \n
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353均勻分布 \n
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354二項分布 \n
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36習題 \n
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第4章Pandas——數(shù)據(jù)處理工具 \n
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41認識Pandas \n
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42Series \n
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421創(chuàng)建Series \n
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422 Series屬性 \n
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423訪問Series數(shù)據(jù) \n
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43操作Series \n
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431更新Series \n
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432插入Series \n
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433刪除Series \n
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44DataFrame \n
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441創(chuàng)建DataFrame \n
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442DataFrame屬性 \n
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443選取行列數(shù)據(jù) \n
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45操作DataFrame \n
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451更新DataFrame \n
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452插入DataFrame \n
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453刪除DataFrame \n
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46Index \n
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461創(chuàng)建Index \n
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462常用屬性 \n
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463常用方法 \n
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464重建Index \n
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47可視化 \n
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471線形圖 \n
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472條形圖 \n
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473餅狀圖 \n
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474直方圖與密度圖 \n
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48數(shù)據(jù)轉(zhuǎn)換 \n
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481數(shù)據(jù)值替換 \n
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482數(shù)據(jù)映射 \n
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483數(shù)據(jù)值合并 \n
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484數(shù)據(jù)值補充 \n
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485數(shù)據(jù)離散化 \n
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49數(shù)據(jù)分組與聚合 \n
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491數(shù)據(jù)分組 \n
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492數(shù)據(jù)聚合 \n
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410讀取外部數(shù)據(jù) \n
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4101操作Excel \n
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4102操作文本文件 \n
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4103操作數(shù)據(jù)庫 \n
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411習題 \n
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第5章Scipy——數(shù)據(jù)統(tǒng)計工具 \n
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51認識Scipy \n
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52稀疏矩陣 \n
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53線性代數(shù) \n
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531矩陣運算 \n
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532線性方程組求解 \n
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54數(shù)據(jù)優(yōu)化 \n
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541非線性方程組求解 \n
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542函數(shù)最值 \n
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543最小二乘法 \n
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55數(shù)據(jù)分布 \n
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551泊松分布 \n
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552正態(tài)分布 \n
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553均勻分布 \n
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554二項分布 \n
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555指數(shù)分布 \n
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56統(tǒng)計量 \n
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561眾數(shù) \n
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562皮爾森相關系數(shù) \n
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57圖像處理 \n
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571旋轉(zhuǎn)圖像 \n
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572圖像濾波 \n
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573邊緣檢測 \n
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58習題 \n
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第6章Seaborn——數(shù)據(jù)可視化 \n
工具 \n
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61認識Seaborn \n
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611繪圖特色 \n
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612圖表分類 \n
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613數(shù)據(jù)集 \n
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62繪圖設置 \n
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621繪圖元素 \n
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622主題 \n
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623調(diào)色板 \n
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63繪圖 \n
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631直方圖 \n
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632核密度圖 \n
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633小提琴圖 \n
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634分類散點圖 \n
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635條形圖 \n
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636熱力圖 \n
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637點圖 \n
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64習題 \n
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第7章Sklearn——機器學習工具 \n
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71Sklearn簡介 \n
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72安裝Sklearn \n
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73數(shù)據(jù)集 \n
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731小數(shù)據(jù)集 \n
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732大數(shù)據(jù)集 \n
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733生成數(shù)據(jù)集 \n
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74機器學習流程 \n
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741數(shù)據(jù)清洗 \n
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742劃分數(shù)據(jù)集 \n
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743特征工程 \n
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744機器算法 \n
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745模型評估 \n
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75習題 \n
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第8章數(shù)據(jù)處理 \n
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81認識數(shù)據(jù)處理 \n
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82數(shù)據(jù)清洗 \n
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821處理缺失值 \n
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822處理異常值 \n
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823處理重復值 \n
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83特征處理 \n
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831規(guī)范化 \n
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832標準化 \n
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833魯棒化 \n
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84數(shù)據(jù)分析可視庫 \n
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841missingno庫 \n
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842詞云 \n
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85案例——學生信息清洗 \n
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86習題 \n
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第9章特征工程 \n
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91認識特征工程 \n
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92獨熱編碼 \n
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93特征提取 \n
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931DictVectorizer \n
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932CountVectorizer \n
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933TfidfVectorizer \n
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94中文分詞 \n
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941Jieba分詞庫 \n
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942停用詞表 \n
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95案例——中文特征提取 \n
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96習題 \n
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第10章評價指標 \n
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