{"id":73872,"date":"2026-02-08T14:49:43","date_gmt":"2026-02-08T06:49:43","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/73872.html"},"modified":"2026-02-08T14:49:43","modified_gmt":"2026-02-08T06:49:43","slug":"%e9%9b%b6%e5%9f%ba%e7%a1%80%e5%ad%a6-ai%ef%bc%9aai-%e5%9f%ba%e7%a1%80%e8%83%bd%e5%8a%9b%e5%a4%af%e5%ae%9e-%e7%bc%96%e7%a8%8b%e8%af%ad%e8%a8%80%e4%b8%8e%e5%b7%a5%e5%85%b7%e7%af%87","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/73872.html","title":{"rendered":"\u96f6\u57fa\u7840\u5b66 AI\uff1aAI \u57fa\u7840\u80fd\u529b\u592f\u5b9e \u2014\u2014 \u7f16\u7a0b\u8bed\u8a00\u4e0e\u5de5\u5177\u7bc7"},"content":{"rendered":"<p style=\"text-align:center\"><img decoding=\"async\" alt=\"\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/02\/20260208064942-698832060a42a.jpg\" \/><\/p>\n<h2>\u96f6\u57fa\u7840\u5b66 AI&#xff1a;AI \u57fa\u7840\u80fd\u529b\u592f\u5b9e \u2014\u2014 \u7f16\u7a0b\u8bed\u8a00\u4e0e\u5de5\u5177\u7bc7<\/h2>\n<h3>\u524d\u8a00<\/h3>\n<p>\u5982\u679c\u8bf4\u6570\u5b66\u662f AI \u7684 \u201c\u5e95\u5c42\u903b\u8f91\u201d&#xff0c;\u90a3\u4e48\u7f16\u7a0b\u8bed\u8a00\u4e0e\u5de5\u5177\u5c31\u662f AI \u7684 \u201c\u5b9e\u6218\u53cc\u624b\u201d\u3002AI \u7684\u6838\u5fc3\u662f \u201c\u6570\u636e &#043; \u7b97\u6cd5\u201d&#xff0c;\u800c Python \u51ed\u501f\u7b80\u6d01\u7684\u8bed\u6cd5\u3001\u4e30\u5bcc\u7684\u5f00\u6e90\u5e93\u751f\u6001&#xff0c;\u6210\u4e3a AI \u9886\u57df\u7684 \u201c\u7b2c\u4e00\u8bed\u8a00\u201d\u2014\u2014 \u4ece\u6570\u636e\u8bfb\u53d6\u3001\u9884\u5904\u7406&#xff0c;\u5230\u6a21\u578b\u8bad\u7ec3\u3001\u53ef\u89c6\u5316&#xff0c;Python \u53ca\u5176\u751f\u6001\u5de5\u5177\u80fd\u4e00\u7ad9\u5f0f\u5b8c\u6210\u6240\u6709\u6d41\u7a0b\u3002<\/p>\n<p>\u5bf9\u4e8e\u96f6\u57fa\u7840\u540c\u5b66\u6765\u8bf4&#xff0c;\u65e0\u9700\u638c\u63e1\u6240\u6709\u7f16\u7a0b\u77e5\u8bc6&#xff0c;\u91cd\u70b9\u805a\u7126 AI \u5b9e\u6218\u4e2d\u9ad8\u9891\u4f7f\u7528\u7684\u6838\u5fc3\u8bed\u6cd5\u3001\u6570\u636e\u5904\u7406\u5e93\u548c\u5f00\u53d1\u5de5\u5177&#xff0c;\u5c31\u80fd\u5feb\u901f\u5177\u5907 AI \u5b9e\u6218\u7684\u57fa\u7840\u80fd\u529b\u3002\u672c\u6587\u5c06\u6452\u5f03\u590d\u6742\u7684\u7f16\u7a0b\u7406\u8bba&#xff0c;\u76f4\u51fb AI \u573a\u666f\u4e0b\u7684\u6838\u5fc3\u5e94\u7528&#xff0c;\u5e2e\u52a9\u4f60\u9ad8\u6548\u638c\u63e1 \u201c\u80fd\u76f4\u63a5\u7528\u5728 AI \u9879\u76ee\u4e2d\u7684\u7f16\u7a0b\u6280\u80fd\u201d&#xff0c;\u4e3a\u540e\u7eed\u7b97\u6cd5\u5b9e\u6218\u6253\u4e0b\u575a\u5b9e\u7684\u5de5\u5177\u57fa\u7840\u3002<\/p>\n<h3>\u4e00\u3001Python \u6838\u5fc3&#xff1a;AI \u7f16\u7a0b\u7684 \u201c\u57fa\u672c\u529f\u201d<\/h3>\n<p>Python \u8bed\u6cd5\u7b80\u6d01\u3001\u4e0a\u624b\u5bb9\u6613&#xff0c;\u4e14 AI \u9886\u57df\u7684\u6240\u6709\u4e3b\u6d41\u5e93&#xff08;NumPy\u3001Pandas\u3001TensorFlow \u7b49&#xff09;\u90fd\u57fa\u4e8e Python \u5f00\u53d1\u3002\u5b66\u4e60 Python \u65e0\u9700\u9677\u5165 \u201c\u8bed\u6cd5\u7ec6\u8282\u9677\u9631\u201d&#xff0c;\u91cd\u70b9\u638c\u63e1 AI \u573a\u666f\u4e2d\u9ad8\u9891\u4f7f\u7528\u7684\u6838\u5fc3\u7279\u6027&#xff0c;\u5c31\u80fd\u6ee1\u8db3\u5b9e\u6218\u9700\u6c42\u3002<\/p>\n<h4>1.1 \u6838\u5fc3\u6570\u636e\u7c7b\u578b&#xff1a;AI \u4e2d\u7684\u6570\u636e\u8f7d\u4f53<\/h4>\n<p>\u6570\u636e\u662f AI \u7684\u6838\u5fc3&#xff0c;Python \u7684\u57fa\u7840\u6570\u636e\u7c7b\u578b\u76f4\u63a5\u5bf9\u5e94 AI \u4e2d\u7684\u6570\u636e\u5b58\u50a8\u5f62\u5f0f&#xff0c;\u91cd\u70b9\u638c\u63e1\u4ee5\u4e0b 5 \u7c7b&#xff1a;<\/p>\n<ul>\n<li>\u6570\u503c\u578b&#xff08;int\/float&#xff09;&#xff1a;\u5bf9\u5e94 AI \u4e2d\u7684\u7279\u5f81\u503c&#xff08;\u5982\u5e74\u9f84\u3001\u8eab\u9ad8\u3001\u6a21\u578b\u53c2\u6570&#xff09;&#xff0c;\u662f\u6700\u57fa\u7840\u7684\u6570\u636e\u7c7b\u578b&#xff0c;\u652f\u6301\u52a0\u51cf\u4e58\u9664\u3001\u5e42\u8fd0\u7b97\u7b49\u5e38\u89c4\u64cd\u4f5c\u3002<\/li>\n<li>\u5b57\u7b26\u4e32&#xff08;str&#xff09;&#xff1a;\u5bf9\u5e94 AI \u4e2d\u7684\u6587\u672c\u6570\u636e&#xff08;\u5982\u7528\u6237\u8bc4\u8bba\u3001\u65b0\u95fb\u6587\u672c&#xff09;&#xff0c;\u6838\u5fc3\u638c\u63e1\u5b57\u7b26\u4e32\u7684\u5207\u7247&#xff08;\u63d0\u53d6\u90e8\u5206\u6587\u672c&#xff09;\u3001\u62fc\u63a5&#xff08;\u7ec4\u5408\u6587\u672c&#xff09;\u3001\u66ff\u6362&#xff08;\u6570\u636e\u6e05\u6d17&#xff09;\u64cd\u4f5c&#xff0c;\u4e3a\u540e\u7eed\u6587\u672c\u5904\u7406\u6253\u57fa\u7840\u3002<\/li>\n<li>\u5217\u8868&#xff08;list&#xff09;&#xff1a;\u52a8\u6001\u6570\u7ec4&#xff0c;\u53ef\u5b58\u50a8\u4e0d\u540c\u7c7b\u578b\u6570\u636e&#xff0c;\u5bf9\u5e94 AI \u4e2d\u7684 \u201c\u6837\u672c\u5217\u8868\u201d\u201c\u7279\u5f81\u5217\u8868\u201d&#xff08;\u5982\u591a\u4e2a\u6837\u672c\u7684\u7279\u5f81\u96c6\u5408&#xff09;&#xff0c;\u6838\u5fc3\u638c\u63e1\u7d22\u5f15\u8bbf\u95ee\u3001append&#xff08;\u6dfb\u52a0\u5143\u7d20&#xff09;\u3001extend&#xff08;\u6269\u5c55\u5217\u8868&#xff09;\u3001\u5207\u7247\u64cd\u4f5c\u3002<\/li>\n<li>\u5143\u7ec4&#xff08;tuple&#xff09;&#xff1a;\u4e0d\u53ef\u53d8\u5217\u8868&#xff0c;\u5e38\u7528\u4e8e\u5b58\u50a8\u56fa\u5b9a\u4e0d\u53d8\u7684\u7279\u5f81&#xff08;\u5982\u7c7b\u522b\u6807\u7b7e\u3001\u5750\u6807\u6570\u636e&#xff09;&#xff0c;\u6838\u5fc3\u638c\u63e1\u7d22\u5f15\u8bbf\u95ee\u548c\u6253\u5305 \/ \u89e3\u5305&#xff08;\u5982\u51fd\u6570\u591a\u8fd4\u56de\u503c\u63a5\u6536&#xff09;\u3002<\/li>\n<li>\u5b57\u5178&#xff08;dict&#xff09;&#xff1a;\u952e\u503c\u5bf9\u7ed3\u6784&#xff0c;\u5bf9\u5e94 AI \u4e2d\u7684 \u201c\u7279\u5f81 &#8211; \u503c\u6620\u5c04\u201d&#xff08;\u5982\u7528\u6237 ID &#8211; \u884c\u4e3a\u7279\u5f81\u3001\u7c7b\u522b\u540d\u79f0 &#8211; \u7f16\u7801\u503c&#xff09;&#xff0c;\u6838\u5fc3\u638c\u63e1\u952e\u8bbf\u95ee\u3001get&#xff08;\u5b89\u5168\u83b7\u53d6\u503c&#xff09;\u3001items&#xff08;\u904d\u5386\u952e\u503c\u5bf9&#xff09;\u64cd\u4f5c&#xff0c;\u662f\u6570\u636e\u9884\u5904\u7406\u4e2d\u5e38\u7528\u7684\u6620\u5c04\u5de5\u5177\u3002<\/li>\n<\/ul>\n<h4>1.2 \u51fd\u6570&#xff1a;AI \u4ee3\u7801\u7684 \u201c\u6a21\u5757\u5316\u5de5\u5177\u201d<\/h4>\n<p>\u51fd\u6570\u662f\u4ee3\u7801\u590d\u7528\u7684\u6838\u5fc3&#xff0c;AI \u4e2d\u5927\u91cf\u91cd\u590d\u64cd\u4f5c&#xff08;\u5982\u6570\u636e\u6807\u51c6\u5316\u3001\u7279\u5f81\u63d0\u53d6&#xff09;\u90fd\u901a\u8fc7\u51fd\u6570\u5b9e\u73b0&#xff0c;\u91cd\u70b9\u638c\u63e1&#xff1a;<\/p>\n<ul>\n<li>\u51fd\u6570\u5b9a\u4e49\u4e0e\u8c03\u7528&#xff1a;\u7528def\u5173\u952e\u5b57\u5b9a\u4e49\u51fd\u6570&#xff0c;\u660e\u786e\u53c2\u6570&#xff08;\u5982\u8f93\u5165\u6570\u636e\u3001\u8d85\u53c2\u6570&#xff09;\u548c\u8fd4\u56de\u503c&#xff08;\u5982\u5904\u7406\u540e\u7684\u6570\u636e\u3001\u6a21\u578b\u7ed3\u679c&#xff09;&#xff0c;\u4f8b\u5982\u5b9a\u4e49\u4e00\u4e2a \u201c\u6570\u636e\u6807\u51c6\u5316\u201d \u51fd\u6570&#xff0c;\u8f93\u5165\u7279\u5f81\u5217\u8868&#xff0c;\u8fd4\u56de\u6807\u51c6\u5316\u540e\u7684\u5217\u8868\u3002<\/li>\n<li>\u9ed8\u8ba4\u53c2\u6570\u4e0e\u5173\u952e\u5b57\u53c2\u6570&#xff1a;AI \u4e2d\u51fd\u6570\u5e38\u9700\u8bbe\u7f6e\u8d85\u53c2\u6570&#xff08;\u5982\u5b66\u4e60\u7387\u3001\u7a97\u53e3\u5927\u5c0f&#xff09;&#xff0c;\u9ed8\u8ba4\u53c2\u6570\u53ef\u7b80\u5316\u8c03\u7528&#xff08;\u5982def normalize(data, mean&#061;0, std&#061;1)&#xff09;&#xff0c;\u5173\u952e\u5b57\u53c2\u6570\u53ef\u63d0\u9ad8\u4ee3\u7801\u53ef\u8bfb\u6027&#xff08;\u5982normalize(data, mean&#061;5, std&#061;2)&#xff09;\u3002<\/li>\n<li>\u533f\u540d\u51fd\u6570&#xff08;lambda&#xff09;&#xff1a;\u7b80\u6d01\u7684\u5355\u884c\u51fd\u6570&#xff0c;\u5e38\u7528\u4e8emap\u3001filter\u7b49\u51fd\u6570\u5f0f\u7f16\u7a0b\u573a\u666f&#xff0c;\u6216\u4f5c\u4e3a\u6392\u5e8f key&#xff08;\u5982\u6309\u5b57\u5178\u7684\u67d0\u4e2a\u503c\u6392\u5e8f&#xff09;&#xff0c;AI \u4e2d\u5e38\u7528\u4e8e\u5feb\u901f\u5904\u7406\u7b80\u5355\u903b\u8f91&#xff08;\u5982\u7279\u5f81\u8f6c\u6362&#xff09;\u3002<\/li>\n<li>\u51fd\u6570\u8fd4\u56de\u503c&#xff1a;\u652f\u6301\u591a\u8fd4\u56de\u503c&#xff08;\u7528\u9017\u53f7\u5206\u9694&#xff09;&#xff0c;AI \u4e2d\u5e38\u7528\u4e8e\u540c\u65f6\u8fd4\u56de\u5904\u7406\u540e\u7684\u6570\u636e\u548c\u7edf\u8ba1\u4fe1\u606f&#xff08;\u5982\u8fd4\u56de\u8bad\u7ec3\u96c6\u3001\u6d4b\u8bd5\u96c6\u548c\u6570\u636e\u96c6\u5747\u503c&#xff09;\u3002<\/li>\n<\/ul>\n<h4>1.3 \u7c7b\u4e0e\u9762\u5411\u5bf9\u8c61&#xff1a;\u590d\u6742 AI \u6a21\u578b\u7684 \u201c\u5c01\u88c5\u65b9\u5f0f\u201d<\/h4>\n<p>AI \u4e2d\u7684\u590d\u6742\u6a21\u578b&#xff08;\u5982\u795e\u7ecf\u7f51\u7edc\u3001\u96c6\u6210\u5b66\u4e60\u7b97\u6cd5&#xff09;\u901a\u5e38\u4ee5\u7c7b\u7684\u5f62\u5f0f\u5c01\u88c5&#xff0c;\u91cd\u70b9\u638c\u63e1\u6838\u5fc3\u6982\u5ff5&#xff0c;\u65e0\u9700\u6df1\u5165\u9762\u5411\u5bf9\u8c61\u7684\u590d\u6742\u7279\u6027&#xff1a;<\/p>\n<ul>\n<li>\u7c7b\u7684\u5b9a\u4e49\u4e0e\u5b9e\u4f8b\u5316&#xff1a;\u7528class\u5173\u952e\u5b57\u5b9a\u4e49\u7c7b&#xff0c;\u5c01\u88c5\u5c5e\u6027&#xff08;\u5982\u6a21\u578b\u53c2\u6570\u3001\u6570\u636e&#xff09;\u548c\u65b9\u6cd5&#xff08;\u5982\u8bad\u7ec3\u65b9\u6cd5\u3001\u9884\u6d4b\u65b9\u6cd5&#xff09;&#xff0c;\u5b9e\u4f8b\u5316\u540e\u901a\u8fc7\u5bf9\u8c61\u8c03\u7528\u65b9\u6cd5&#xff08;\u5982model &#061; LinearRegression()&#xff0c;model.train(X, y)&#xff09;\u3002<\/li>\n<li>\u521d\u59cb\u5316\u65b9\u6cd5&#xff08;init&#xff09;&#xff1a;\u7c7b\u7684\u6838\u5fc3\u65b9\u6cd5&#xff0c;\u7528\u4e8e\u521d\u59cb\u5316\u5bf9\u8c61\u5c5e\u6027&#xff08;\u5982\u6a21\u578b\u7684\u6743\u91cd\u3001\u504f\u7f6e\u3001\u5b66\u4e60\u7387&#xff09;&#xff0c;\u4f8b\u5982\u5728\u56de\u5f52\u6a21\u578b\u7c7b\u4e2d&#xff0c;__init__\u65b9\u6cd5\u521d\u59cb\u5316\u6743\u91cd\u4e3a\u968f\u673a\u503c\u3002<\/li>\n<li>\u5b9e\u4f8b\u65b9\u6cd5\u4e0e\u7c7b\u65b9\u6cd5&#xff1a;\u5b9e\u4f8b\u65b9\u6cd5&#xff08;\u7b2c\u4e00\u4e2a\u53c2\u6570\u4e3aself&#xff09;\u64cd\u4f5c\u5bf9\u8c61\u5c5e\u6027&#xff08;\u5982\u8bad\u7ec3\u65b9\u6cd5\u66f4\u65b0self.weight&#xff09;&#xff0c;\u7c7b\u65b9\u6cd5&#xff08;\u7528&#064;classmethod\u88c5\u9970&#xff09;\u7528\u4e8e\u5904\u7406\u7c7b\u7ea7\u522b\u7684\u903b\u8f91&#xff08;\u5982\u52a0\u8f7d\u9884\u8bad\u7ec3\u6a21\u578b\u53c2\u6570&#xff09;\u3002<\/li>\n<li>\u7ee7\u627f\u4e0e\u591a\u6001&#xff1a;AI \u4e2d\u5e38\u7528\u7ee7\u627f\u5b9e\u73b0\u6a21\u578b\u6269\u5c55&#xff08;\u5982\u57fa\u4e8e \u201cBaseModel\u201d \u7c7b\u6269\u5c55 \u201cClassificationModel\u201d\u201cRegressionModel\u201d&#xff09;&#xff0c;\u6838\u5fc3\u7406\u89e3 \u201c\u5b50\u7c7b\u590d\u7528\u7236\u7c7b\u65b9\u6cd5&#xff0c;\u5e76\u91cd\u5199\u81ea\u5b9a\u4e49\u903b\u8f91\u201d \u5373\u53ef\u3002<\/li>\n<\/ul>\n<h4>1.4 \u88c5\u9970\u5668&#xff1a;AI \u4e2d\u7684 \u201c\u529f\u80fd\u589e\u5f3a\u5de5\u5177\u201d<\/h4>\n<p>\u88c5\u9970\u5668\u662f Python \u7684\u9ad8\u7ea7\u7279\u6027&#xff0c;\u65e0\u9700\u638c\u63e1\u5e95\u5c42\u5b9e\u73b0&#xff0c;\u91cd\u70b9\u7406\u89e3\u5176\u5728 AI \u4e2d\u7684\u5e94\u7528\u573a\u666f&#xff1a;<\/p>\n<ul>\n<li>\u6838\u5fc3\u4f5c\u7528&#xff1a;\u5728\u4e0d\u4fee\u6539\u539f\u51fd\u6570\u4ee3\u7801\u7684\u524d\u63d0\u4e0b&#xff0c;\u4e3a\u51fd\u6570\u6dfb\u52a0\u989d\u5916\u529f\u80fd&#xff08;\u5982\u8ba1\u65f6\u3001\u65e5\u5fd7\u3001\u7f13\u5b58\u3001\u53c2\u6570\u6821\u9a8c&#xff09;\u3002<\/li>\n<li>AI \u4e2d\u7684\u9ad8\u9891\u5e94\u7528&#xff1a;\n<li>\u6a21\u578b\u8bad\u7ec3\u8ba1\u65f6&#xff1a;\u88c5\u9970\u5668\u8bb0\u5f55\u8bad\u7ec3\u51fd\u6570\u7684\u6267\u884c\u65f6\u95f4&#xff0c;\u7528\u4e8e\u8bc4\u4f30\u8bad\u7ec3\u6548\u7387\u3002<\/li>\n<li>\u7f13\u5b58\u7ed3\u679c&#xff1a;\u88c5\u9970\u5668\u7f13\u5b58\u51fd\u6570&#xff08;\u5982\u6570\u636e\u9884\u5904\u7406\u51fd\u6570&#xff09;\u7684\u8f93\u51fa&#xff0c;\u907f\u514d\u91cd\u590d\u8ba1\u7b97&#xff0c;\u63d0\u5347\u6548\u7387\u3002<\/li>\n<li>\u65e5\u5fd7\u8bb0\u5f55&#xff1a;\u88c5\u9970\u5668\u8bb0\u5f55\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u5173\u952e\u4fe1\u606f&#xff08;\u5982\u53c2\u6570\u3001\u635f\u5931\u503c&#xff09;&#xff0c;\u4fbf\u4e8e\u8c03\u8bd5\u3002<\/li>\n<\/li>\n<li>\u5e38\u7528\u5de5\u5177&#xff1a;\u65e0\u9700\u624b\u52a8\u7f16\u5199\u590d\u6742\u88c5\u9970\u5668&#xff0c;\u4f7f\u7528functools\u6a21\u5757\u7684lru_cache&#xff08;\u7f13\u5b58&#xff09;\u3001timeit\u6a21\u5757\u7684\u8ba1\u65f6\u529f\u80fd&#xff0c;\u6216\u7b2c\u4e09\u65b9\u5e93&#xff08;\u5982decorator&#xff09;\u5373\u53ef\u6ee1\u8db3\u9700\u6c42\u3002<\/li>\n<\/ul>\n<h4>1.5 \u751f\u6210\u5668&#xff1a;\u5927\u6570\u636e\u573a\u666f\u7684 \u201c\u5185\u5b58\u4f18\u5316\u5de5\u5177\u201d<\/h4>\n<p>AI \u4e2d\u5e38\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u96c6&#xff08;\u5982\u767e\u4e07\u7ea7\u6837\u672c\u3001GB \u7ea7\u6587\u672c&#xff09;&#xff0c;\u751f\u6210\u5668\u80fd\u6709\u6548\u8282\u7701\u5185\u5b58&#xff0c;\u91cd\u70b9\u638c\u63e1&#xff1a;<\/p>\n<ul>\n<li>\u6838\u5fc3\u7279\u6027&#xff1a;\u751f\u6210\u5668\u901a\u8fc7yield\u5173\u952e\u5b57\u8fd4\u56de\u6570\u636e&#xff0c;\u6bcf\u6b21\u4ec5\u751f\u6210\u4e00\u4e2a\u5143\u7d20&#xff0c;\u4e0d\u5360\u7528\u5927\u91cf\u5185\u5b58&#xff08;\u533a\u522b\u4e8e\u5217\u8868\u4e00\u6b21\u6027\u52a0\u8f7d\u6240\u6709\u6570\u636e&#xff09;\u3002<\/li>\n<li>AI \u4e2d\u7684\u5e94\u7528\u573a\u666f&#xff1a;\n<li>\u5927\u89c4\u6a21\u6570\u636e\u8bfb\u53d6&#xff1a;\u9010\u884c\u8bfb\u53d6\u6587\u672c\u6587\u4ef6\u3001\u9010\u6279\u52a0\u8f7d\u56fe\u50cf\u6570\u636e&#xff0c;\u907f\u514d\u5185\u5b58\u6ea2\u51fa\u3002<\/li>\n<li>\u6570\u636e\u751f\u6210&#xff1a;\u52a8\u6001\u751f\u6210\u8bad\u7ec3\u6837\u672c&#xff08;\u5982\u6570\u636e\u589e\u5f3a\u65f6\u5b9e\u65f6\u751f\u6210\u53d8\u5f62\u56fe\u50cf&#xff09;&#xff0c;\u9002\u7528\u4e8e\u6570\u636e\u91cf\u8fc7\u5927\u65e0\u6cd5\u4e00\u6b21\u6027\u52a0\u8f7d\u7684\u573a\u666f\u3002<\/li>\n<\/li>\n<li>\u4f7f\u7528\u65b9\u5f0f&#xff1a;\u901a\u8fc7\u51fd\u6570 &#043;yield\u5b9a\u4e49\u751f\u6210\u5668&#xff0c;\u6216\u7528\u751f\u6210\u5668\u8868\u8fbe\u5f0f&#xff08;\u5982(x*2 for x in range(1000000))&#xff09;&#xff0c;\u8fed\u4ee3\u65f6\u7528for\u5faa\u73af\u9010\u6b21\u83b7\u53d6\u5143\u7d20\u3002<\/li>\n<\/ul>\n<h4>Python \u5b66\u4e60\u91cd\u70b9<\/h4>\n<p>\u653e\u5f03 \u201c\u5168\u91cf\u5b66\u4e60\u201d&#xff0c;\u805a\u7126 **\u201cAI \u573a\u666f\u9ad8\u9891\u7528\u6cd5\u201d**&#xff1a;\u6bd4\u5982\u5217\u8868\u3001\u5b57\u5178\u7684\u64cd\u4f5c\u8981\u719f\u7ec3&#xff0c;\u51fd\u6570\u8981\u4f1a\u5b9a\u4e49\u548c\u4f20\u53c2&#xff0c;\u7c7b\u8981\u7406\u89e3\u5c01\u88c5\u548c\u5b9e\u4f8b\u5316&#xff0c;\u88c5\u9970\u5668\u548c\u751f\u6210\u5668\u77e5\u9053\u5e94\u7528\u573a\u666f\u5e76\u4f1a\u7b80\u5355\u4f7f\u7528\u5373\u53ef\u3002\u65e0\u9700\u7ea0\u7ed3\u8bed\u6cd5\u7ec6\u8282&#xff08;\u5982\u5143\u7c7b\u3001\u590d\u6742\u7ee7\u627f&#xff09;&#xff0c;\u80fd\u9ad8\u6548\u7528 Python \u5904\u7406\u6570\u636e\u3001\u8c03\u7528\u5de5\u5177\u5e93\u5c31\u662f\u6838\u5fc3\u76ee\u6807\u3002<\/p>\n<h3>\u4e8c\u3001\u6570\u636e\u5904\u7406\u5e93&#xff1a;AI \u5b9e\u6218\u7684 \u201c\u6838\u5fc3\u6b66\u5668\u201d<\/h3>\n<p>AI \u7684\u7b2c\u4e00\u6b65\u662f \u201c\u6570\u636e\u5904\u7406\u201d\u2014\u2014 \u73b0\u5b9e\u4e2d\u7684\u6570\u636e\u5f80\u5f80\u662f\u6742\u4e71\u65e0\u7ae0\u7684&#xff08;\u5982\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u3001\u683c\u5f0f\u4e0d\u7edf\u4e00&#xff09;&#xff0c;\u9700\u8981\u901a\u8fc7\u5e93\u5de5\u5177\u8fdb\u884c\u6e05\u6d17\u3001\u8f6c\u6362\u3001\u5206\u6790\u548c\u53ef\u89c6\u5316\u3002AI \u9886\u57df\u6700\u6838\u5fc3\u7684\u4e09\u5927\u5e93\u662fNumPy&#xff08;\u6570\u503c\u8ba1\u7b97&#xff09;\u3001Pandas&#xff08;\u6570\u636e\u5904\u7406&#xff09;\u3001Matplotlib\/Seaborn&#xff08;\u6570\u636e\u53ef\u89c6\u5316&#xff09;&#xff0c;\u4e09\u8005\u914d\u5408\u80fd\u5b8c\u6210\u4ece\u6570\u636e\u8bfb\u53d6\u5230\u5206\u6790\u7684\u5168\u6d41\u7a0b\u3002<\/p>\n<h4>2.1 NumPy&#xff1a;\u6570\u503c\u8ba1\u7b97\u7684 \u201c\u57fa\u77f3\u201d<\/h4>\n<p>NumPy \u662f Python \u6570\u503c\u8ba1\u7b97\u7684\u6838\u5fc3\u5e93&#xff0c;\u63d0\u4f9b\u4e86\u9ad8\u6548\u7684\u591a\u7ef4\u6570\u7ec4&#xff08;ndarray&#xff09;\u548c\u77e9\u9635\u8fd0\u7b97\u529f\u80fd&#xff0c;AI \u4e2d\u7684\u6240\u6709\u6570\u503c\u8ba1\u7b97&#xff08;\u5982\u6a21\u578b\u53c2\u6570\u66f4\u65b0\u3001\u7279\u5f81\u77e9\u9635\u8fd0\u7b97&#xff09;\u90fd\u57fa\u4e8e NumPy \u5b9e\u73b0\u3002<\/p>\n<ul>\n<li>\u6838\u5fc3\u4f18\u52bf&#xff1a;\u6bd4 Python \u539f\u751f\u5217\u8868\u5feb 10-100 \u500d&#xff0c;\u652f\u6301\u5411\u91cf\u5316\u8fd0\u7b97&#xff08;\u65e0\u9700\u5faa\u73af&#xff0c;\u76f4\u63a5\u5bf9\u6570\u7ec4\u6574\u4f53\u64cd\u4f5c&#xff09;&#xff0c;\u5927\u5e45\u63d0\u5347\u8ba1\u7b97\u6548\u7387\u3002<\/li>\n<li>AI \u4e2d\u9ad8\u9891\u7528\u6cd5&#xff1a;\n<li>\u591a\u7ef4\u6570\u7ec4&#xff08;ndarray&#xff09;&#xff1a;AI \u4e2d\u6570\u636e\u7684\u6807\u51c6\u5b58\u50a8\u683c\u5f0f&#xff08;\u5982 m \u4e2a\u6837\u672c n \u4e2a\u7279\u5f81\u7684\u6570\u636e\u96c6\u662f m\u00d7n \u7684 ndarray&#xff0c;\u56fe\u50cf\u6570\u636e\u662f H\u00d7W\u00d7C \u7684 ndarray&#xff09;&#xff0c;\u6838\u5fc3\u638c\u63e1\u6570\u7ec4\u521b\u5efa&#xff08;np.array()&#xff09;\u3001\u5f62\u72b6\u67e5\u770b&#xff08;shape&#xff09;\u3001\u7ef4\u5ea6\u8f6c\u6362&#xff08;reshape()&#xff09;\u3001\u7d22\u5f15\u5207\u7247&#xff08;\u5982X[:, 0]\u53d6\u7b2c\u4e00\u5217\u7279\u5f81&#xff09;\u3002<\/li>\n<li>\u5411\u91cf\u5316\u8fd0\u7b97&#xff1a;\u66ff\u4ee3 Python \u5faa\u73af&#xff0c;\u4f8b\u5982\u7279\u5f81\u6807\u51c6\u5316&#xff08;(X &#8211; X.mean(axis&#061;0)) \/ X.std(axis&#061;0)&#xff09;\u3001\u77e9\u9635\u4e58\u6cd5&#xff08;np.dot(X, W)\u6216X &#064; W&#xff0c;\u5bf9\u5e94\u7ebf\u6027\u4ee3\u6570\u4e2d\u7684\u77e9\u9635\u8fd0\u7b97&#xff09;\u3001\u5143\u7d20\u7ea7\u8fd0\u7b97&#xff08;&#043; &#8211; * \/ \u76f4\u63a5\u4f5c\u7528\u4e8e\u6570\u7ec4&#xff09;\u3002<\/li>\n<li>\u7edf\u8ba1\u4e0e\u7ebf\u6027\u4ee3\u6570\u51fd\u6570&#xff1a;np.mean()&#xff08;\u5747\u503c&#xff09;\u3001np.std()&#xff08;\u6807\u51c6\u5dee&#xff09;\u3001np.max()&#xff08;\u6700\u5927\u503c&#xff09;\u7528\u4e8e\u6570\u636e\u7edf\u8ba1&#xff1b;np.linalg.inv()&#xff08;\u9006\u77e9\u9635&#xff09;\u3001np.linalg.eig()&#xff08;\u7279\u5f81\u503c\u5206\u89e3&#xff09;\u3001np.linalg.svd()&#xff08;\u5947\u5f02\u503c\u5206\u89e3&#xff09;\u5bf9\u5e94\u7ebf\u6027\u4ee3\u6570\u8fd0\u7b97&#xff0c;\u662f\u6a21\u578b\u5e95\u5c42\u5b9e\u73b0\u7684\u6838\u5fc3\u3002<\/li>\n<\/li>\n<li>\u5b66\u4e60\u91cd\u70b9&#xff1a;\u719f\u7ec3\u638c\u63e1 ndarray \u7684\u521b\u5efa\u3001\u5f62\u72b6\u64cd\u4f5c\u3001\u5411\u91cf\u5316\u8fd0\u7b97&#xff0c;\u65e0\u9700\u6df1\u5165\u590d\u6742\u7684\u6570\u5b66\u51fd\u6570&#xff0c;\u80fd\u6ee1\u8db3\u6570\u636e\u9884\u5904\u7406\u548c\u77e9\u9635\u8fd0\u7b97\u5373\u53ef\u3002<\/li>\n<\/ul>\n<h4>2.2 Pandas&#xff1a;\u6570\u636e\u5904\u7406\u7684 \u201c\u745e\u58eb\u519b\u5200\u201d<\/h4>\n<p>Pandas \u57fa\u4e8e NumPy \u6784\u5efa&#xff0c;\u4e13\u95e8\u7528\u4e8e\u5904\u7406\u7ed3\u6784\u5316\u6570\u636e&#xff08;\u5982 CSV \u8868\u683c\u3001Excel \u6587\u4ef6\u3001\u6570\u636e\u5e93\u6570\u636e&#xff09;&#xff0c;AI \u4e2d 90% \u7684\u6570\u636e\u8bfb\u53d6\u548c\u9884\u5904\u7406\u90fd\u7528 Pandas \u5b8c\u6210&#xff0c;\u662f\u6570\u636e\u5904\u7406\u7684\u6838\u5fc3\u5de5\u5177\u3002<\/p>\n<ul>\n<li>\u6838\u5fc3\u6570\u636e\u7ed3\u6784&#xff1a;\n<li>Series&#xff1a;\u4e00\u7ef4\u5e26\u6807\u7b7e\u6570\u7ec4&#xff0c;\u5bf9\u5e94 \u201c\u5355\u4e2a\u7279\u5f81\u201d \u6216 \u201c\u7c7b\u522b\u6807\u7b7e\u201d&#xff0c;\u6838\u5fc3\u638c\u63e1\u7d22\u5f15\u8bbf\u95ee\u3001\u7f3a\u5931\u503c\u5224\u65ad&#xff08;isnull()&#xff09;\u3001\u586b\u5145&#xff08;fillna()&#xff09;\u3002<\/li>\n<li>DataFrame&#xff1a;\u4e8c\u7ef4\u8868\u683c\u7ed3\u6784&#xff08;\u884c &#061; \u6837\u672c&#xff0c;\u5217 &#061; \u7279\u5f81&#xff09;&#xff0c;\u5bf9\u5e94 AI \u4e2d\u7684 \u201c\u6570\u636e\u96c6\u201d&#xff0c;\u662f Pandas \u6700\u6838\u5fc3\u7684\u7ed3\u6784&#xff0c;\u6838\u5fc3\u638c\u63e1\u6570\u636e\u8bfb\u53d6&#xff08;read_csv()&#xff09;\u3001\u67e5\u770b&#xff08;head()\/info()\/describe()&#xff09;\u3001\u5217\u64cd\u4f5c&#xff08;\u6dfb\u52a0 \/ \u5220\u9664\u5217\u3001\u91cd\u547d\u540d\u5217&#xff09;\u3002<\/li>\n<\/li>\n<li>AI \u4e2d\u9ad8\u9891\u7528\u6cd5&#xff1a;\n<li>\u6570\u636e\u8bfb\u53d6\u4e0e\u4fdd\u5b58&#xff1a;pd.read_csv()&#xff08;\u8bfb\u53d6 CSV \u6587\u4ef6&#xff09;\u3001pd.read_excel()&#xff08;\u8bfb\u53d6 Excel \u6587\u4ef6&#xff09;\u3001df.to_csv()&#xff08;\u4fdd\u5b58\u6570\u636e&#xff09;&#xff0c;\u652f\u6301\u5904\u7406\u7f3a\u5931\u503c\u3001\u6307\u5b9a\u5217\u7c7b\u578b&#xff0c;\u662f AI \u9879\u76ee\u4e2d\u6570\u636e\u8f93\u5165\u8f93\u51fa\u7684\u6807\u51c6\u65b9\u5f0f\u3002<\/li>\n<li>\u6570\u636e\u6e05\u6d17&#xff1a;\u5904\u7406\u7f3a\u5931\u503c&#xff08;df.dropna()\u5220\u9664 \/df.fillna()\u586b\u5145&#xff09;\u3001\u5904\u7406\u5f02\u5e38\u503c&#xff08;df[(df[&#039;col&#039;] &gt; lower) &amp; (df[&#039;col&#039;] &lt; upper)]\u8fc7\u6ee4&#xff09;\u3001\u6570\u636e\u53bb\u91cd&#xff08;df.drop_duplicates()&#xff09;&#xff0c;\u662f\u6570\u636e\u9884\u5904\u7406\u7684\u6838\u5fc3\u6b65\u9aa4\u3002<\/li>\n<li>\u6570\u636e\u8f6c\u6362&#xff1a;\u7279\u5f81\u7f16\u7801&#xff08;pd.get_dummies()\u72ec\u70ed\u7f16\u7801&#xff0c;\u7528\u4e8e\u5206\u7c7b\u7279\u5f81&#xff09;\u3001\u6570\u636e\u5f52\u4e00\u5316 \/ \u6807\u51c6\u5316&#xff08;df[&#039;col&#039;] &#061; (df[&#039;col&#039;] &#8211; df[&#039;col&#039;].mean()) \/ df[&#039;col&#039;].std()&#xff09;\u3001\u6570\u636e\u5408\u5e76&#xff08;pd.merge()\u5173\u8054\u591a\u4e2a\u8868\u683c&#xff0c;\u5982\u7528\u6237\u8868\u548c\u884c\u4e3a\u8868&#xff09;\u3002<\/li>\n<li>\u6570\u636e\u7b5b\u9009\u4e0e\u5206\u7ec4&#xff1a;df[df[&#039;age&#039;] &gt; 18]\u7b5b\u9009\u6837\u672c\u3001df.groupby(&#039;category&#039;)[&#039;value&#039;].mean()\u6309\u7c7b\u522b\u5206\u7ec4\u7edf\u8ba1&#xff0c;\u7528\u4e8e\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790&#xff08;EDA&#xff09;\u3002<\/li>\n<\/li>\n<li>\u5b66\u4e60\u91cd\u70b9&#xff1a;\u719f\u7ec3\u638c\u63e1 DataFrame \u7684\u8bfb\u53d6\u3001\u6e05\u6d17\u3001\u8f6c\u6362\u3001\u7b5b\u9009\u64cd\u4f5c&#xff0c;\u5c24\u5176\u662f\u7f3a\u5931\u503c\u5904\u7406\u3001\u7279\u5f81\u7f16\u7801\u3001\u6570\u636e\u5408\u5e76&#xff0c;\u8fd9\u4e9b\u662f AI \u6570\u636e\u9884\u5904\u7406\u7684\u5fc5\u5907\u6280\u80fd\u3002<\/li>\n<\/ul>\n<h4>2.3 Matplotlib\/Seaborn&#xff1a;\u6570\u636e\u53ef\u89c6\u5316\u7684 \u201c\u7a97\u53e3\u201d<\/h4>\n<p>AI \u4e2d\u7684 \u201c\u63a2\u7d22\u6027\u6570\u636e\u5206\u6790&#xff08;EDA&#xff09;\u201d \u548c \u201c\u7ed3\u679c\u5c55\u793a\u201d \u90fd\u79bb\u4e0d\u5f00\u53ef\u89c6\u5316 \u2014\u2014 \u901a\u8fc7\u56fe\u8868\u80fd\u5feb\u901f\u53d1\u73b0\u6570\u636e\u89c4\u5f8b&#xff08;\u5982\u7279\u5f81\u5206\u5e03\u3001\u76f8\u5173\u6027&#xff09;\u3001\u76d1\u63a7\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b&#xff08;\u5982\u635f\u5931\u66f2\u7ebf&#xff09;\u3001\u5c55\u793a\u6a21\u578b\u6548\u679c&#xff08;\u5982\u6df7\u6dc6\u77e9\u9635&#xff09;\u3002<\/p>\n<ul>\n<li>\n<p>Matplotlib&#xff1a;Python \u53ef\u89c6\u5316\u7684\u57fa\u7840\u5e93&#xff0c;\u652f\u6301\u7ed8\u5236\u5404\u79cd\u56fe\u8868&#xff0c;\u7075\u6d3b\u6027\u9ad8&#xff0c;\u662f Seaborn \u7684\u5e95\u5c42\u4f9d\u8d56\u3002<\/p>\n<ul>\n<li>AI \u4e2d\u9ad8\u9891\u56fe\u8868&#xff1a;\n<li>\u76f4\u65b9\u56fe&#xff08;plt.hist()&#xff09;&#xff1a;\u67e5\u770b\u7279\u5f81\u5206\u5e03&#xff08;\u5982\u662f\u5426\u6b63\u6001\u5206\u5e03&#xff09;&#xff0c;\u7528\u4e8e\u6570\u636e\u5206\u5e03\u5206\u6790\u3002<\/li>\n<li>\u6563\u70b9\u56fe&#xff08;plt.scatter()&#xff09;&#xff1a;\u67e5\u770b\u4e24\u4e2a\u7279\u5f81\u7684\u76f8\u5173\u6027&#xff08;\u5982\u662f\u5426\u7ebf\u6027\u76f8\u5173&#xff09;&#xff0c;\u7528\u4e8e\u7279\u5f81\u9009\u62e9\u3002<\/li>\n<li>\u6298\u7ebf\u56fe&#xff08;plt.plot()&#xff09;&#xff1a;\u7ed8\u5236\u6a21\u578b\u8bad\u7ec3\u7684\u635f\u5931\u66f2\u7ebf\u3001\u51c6\u786e\u7387\u66f2\u7ebf&#xff0c;\u76d1\u63a7\u8bad\u7ec3\u8fc7\u7a0b\u3002<\/li>\n<li>\u67f1\u72b6\u56fe&#xff08;plt.bar()&#xff09;&#xff1a;\u5bf9\u6bd4\u4e0d\u540c\u7c7b\u522b\u3001\u4e0d\u540c\u6a21\u578b\u7684\u6548\u679c&#xff08;\u5982\u51c6\u786e\u7387\u3001\u53ec\u56de\u7387&#xff09;\u3002<\/li>\n<\/li>\n<li>\u6838\u5fc3\u64cd\u4f5c&#xff1a;\u8bbe\u7f6e\u6807\u9898&#xff08;plt.title()&#xff09;\u3001\u5750\u6807\u8f74\u6807\u7b7e&#xff08;plt.xlabel()\/plt.ylabel()&#xff09;\u3001\u56fe\u4f8b&#xff08;plt.legend()&#xff09;\u3001\u4fdd\u5b58\u56fe\u7247&#xff08;plt.savefig()&#xff09;&#xff0c;\u8ba9\u56fe\u8868\u66f4\u6e05\u6670\u6613\u8bfb\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>Seaborn&#xff1a;\u57fa\u4e8e Matplotlib \u7684\u9ad8\u7ea7\u5e93&#xff0c;\u8bed\u6cd5\u66f4\u7b80\u6d01&#xff0c;\u56fe\u8868\u66f4\u7f8e\u89c2&#xff0c;\u652f\u6301\u7edf\u8ba1\u53ef\u89c6\u5316&#xff0c;\u662f AI \u5b9e\u6218\u4e2d\u66f4\u5e38\u7528\u7684\u53ef\u89c6\u5316\u5de5\u5177\u3002<\/p>\n<ul>\n<li>AI \u4e2d\u9ad8\u9891\u56fe\u8868&#xff1a;\n<li>\u70ed\u529b\u56fe&#xff08;sns.heatmap()&#xff09;&#xff1a;\u53ef\u89c6\u5316\u7279\u5f81\u76f8\u5173\u6027\u77e9\u9635\u3001\u6df7\u6dc6\u77e9\u9635&#xff0c;\u76f4\u89c2\u5c55\u793a\u53d8\u91cf\u5173\u7cfb\u548c\u6a21\u578b\u5206\u7c7b\u6548\u679c\u3002<\/li>\n<li>\u7bb1\u7ebf\u56fe&#xff08;sns.boxplot()&#xff09;&#xff1a;\u68c0\u6d4b\u7279\u5f81\u7684\u5f02\u5e38\u503c&#xff08;\u5982\u79bb\u7fa4\u70b9&#xff09;&#xff0c;\u7528\u4e8e\u6570\u636e\u6e05\u6d17\u3002<\/li>\n<li>\u914d\u5bf9\u56fe&#xff08;sns.pairplot()&#xff09;&#xff1a;\u5c55\u793a\u591a\u4e2a\u7279\u5f81\u4e4b\u95f4\u7684\u4e24\u4e24\u5173\u7cfb&#xff0c;\u5feb\u901f\u53d1\u73b0\u7279\u5f81\u5173\u8054\u3002<\/li>\n<\/li>\n<li>\u6838\u5fc3\u4f18\u52bf&#xff1a;\u65e0\u9700\u590d\u6742\u914d\u7f6e&#xff0c;\u4e00\u884c\u4ee3\u7801\u5373\u53ef\u751f\u6210\u9ad8\u8d28\u91cf\u56fe\u8868&#xff0c;\u652f\u6301\u4e0e Pandas DataFrame \u65e0\u7f1d\u5bf9\u63a5&#xff08;\u5982df &#061; sns.load_dataset(&#039;iris&#039;)\u76f4\u63a5\u52a0\u8f7d\u793a\u4f8b\u6570\u636e&#xff09;\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u5b66\u4e60\u91cd\u70b9&#xff1a;\u638c\u63e1 \u201c\u5b9e\u7528\u56fe\u8868 &#043; \u6838\u5fc3\u914d\u7f6e\u201d&#xff0c;\u80fd\u7ed8\u5236\u76f4\u65b9\u56fe\u3001\u6563\u70b9\u56fe\u3001\u6298\u7ebf\u56fe\u3001\u70ed\u529b\u56fe&#xff0c;\u4f1a\u8bbe\u7f6e\u56fe\u8868\u6807\u7b7e\u548c\u4fdd\u5b58\u56fe\u7247&#xff0c;\u6ee1\u8db3 EDA \u548c\u6a21\u578b\u7ed3\u679c\u5c55\u793a\u5373\u53ef&#xff0c;\u65e0\u9700\u6df1\u5165\u590d\u6742\u7684\u56fe\u8868\u5b9a\u5236\u3002<\/p>\n<\/li>\n<\/ul>\n<h3>\u4e09\u3001\u5f00\u53d1\u5de5\u5177&#xff1a;AI \u5b9e\u6218\u7684 \u201c\u9ad8\u6548\u73af\u5883\u201d<\/h3>\n<p>\u5408\u9002\u7684\u5f00\u53d1\u5de5\u5177\u80fd\u5927\u5e45\u63d0\u5347 AI \u5b66\u4e60\u548c\u5b9e\u6218\u7684\u6548\u7387&#xff0c;\u65e0\u9700\u7ea0\u7ed3 \u201c\u5de5\u5177\u9009\u62e9\u201d&#xff0c;\u805a\u7126 AI \u9886\u57df\u6700\u4e3b\u6d41\u3001\u6700\u6613\u7528\u7684\u5de5\u5177\u7ec4\u5408&#xff1a;Anaconda&#xff08;\u73af\u5883\u7ba1\u7406&#xff09;\u3001Jupyter Notebook&#xff08;\u4ea4\u4e92\u5f0f\u5f00\u53d1&#xff09;\u3001Git&#xff08;\u7248\u672c\u63a7\u5236&#xff09;\u3002<\/p>\n<h4>3.1 Anaconda&#xff1a;AI \u73af\u5883\u7684 \u201c\u7ba1\u5bb6\u201d<\/h4>\n<p>AI \u9879\u76ee\u9700\u8981\u4f9d\u8d56\u5927\u91cf\u7b2c\u4e09\u65b9\u5e93&#xff08;\u5982 NumPy\u3001TensorFlow&#xff09;&#xff0c;\u4e0d\u540c\u9879\u76ee\u53ef\u80fd\u9700\u8981\u4e0d\u540c\u7248\u672c\u7684\u5e93&#xff08;\u5982 Python 3.8\/3.10\u3001TensorFlow 2.5\/2.10&#xff09;&#xff0c;Anaconda \u80fd\u9ad8\u6548\u7ba1\u7406\u8fd9\u4e9b\u73af\u5883\u548c\u5e93&#xff0c;\u907f\u514d\u7248\u672c\u51b2\u7a81\u3002<\/p>\n<ul>\n<li>\u6838\u5fc3\u7ec4\u4ef6&#xff1a;\n<li>Conda&#xff1a;\u73af\u5883\u7ba1\u7406\u548c\u5305\u7ba1\u7406\u5de5\u5177&#xff0c;\u53ef\u521b\u5efa\u72ec\u7acb\u7684 Python \u73af\u5883&#xff0c;\u5b89\u88c5\u3001\u66f4\u65b0\u3001\u5378\u8f7d\u5e93\u3002<\/li>\n<li>Anaconda Navigator&#xff1a;\u56fe\u5f62\u5316\u754c\u9762&#xff0c;\u65b9\u4fbf\u96f6\u57fa\u7840\u540c\u5b66\u64cd\u4f5c&#xff08;\u65e0\u9700\u547d\u4ee4\u884c&#xff09;\u3002<\/li>\n<li>\u9ed8\u8ba4\u5e93&#xff1a;\u9884\u88c5\u4e86 NumPy\u3001Pandas\u3001Matplotlib \u7b49 AI \u6838\u5fc3\u5e93&#xff0c;\u5b89\u88c5\u540e\u5373\u53ef\u76f4\u63a5\u4f7f\u7528\u3002<\/li>\n<\/li>\n<li>AI \u4e2d\u9ad8\u9891\u64cd\u4f5c&#xff1a;\n<li>\u521b\u5efa\u73af\u5883&#xff1a;conda create -n ai_env python&#061;3.9&#xff08;\u521b\u5efa\u540d\u4e3a ai_env\u3001Python 3.9 \u7684\u73af\u5883&#xff09;&#xff0c;\u9694\u79bb\u4e0d\u540c\u9879\u76ee\u7684\u4f9d\u8d56\u3002<\/li>\n<li>\u6fc0\u6d3b\u73af\u5883&#xff1a;Windows&#xff08;conda activate ai_env&#xff09;\u3001Mac\/Linux&#xff08;source activate ai_env&#xff09;&#xff0c;\u6fc0\u6d3b\u540e\u5b89\u88c5\u7684\u5e93\u4ec5\u4f5c\u7528\u4e8e\u5f53\u524d\u73af\u5883\u3002<\/li>\n<li>\u5b89\u88c5\u5e93&#xff1a;conda install numpy pandas matplotlib&#xff08;conda \u5b89\u88c5&#xff0c;\u4f18\u5148\u63a8\u8350&#xff09;\u6216pip install tensorflow&#xff08;pip \u5b89\u88c5&#xff0c;\u9002\u7528\u4e8e conda \u6ca1\u6709\u7684\u5e93&#xff09;\u3002<\/li>\n<li>\u67e5\u770b\u73af\u5883 \/ \u5e93&#xff1a;conda info &#8211;envs&#xff08;\u67e5\u770b\u6240\u6709\u73af\u5883&#xff09;\u3001conda list&#xff08;\u67e5\u770b\u5f53\u524d\u73af\u5883\u5b89\u88c5\u7684\u5e93&#xff09;\u3002<\/li>\n<\/li>\n<li>\u5b66\u4e60\u91cd\u70b9&#xff1a;\u638c\u63e1 \u201c\u521b\u5efa\u73af\u5883\u3001\u6fc0\u6d3b\u73af\u5883\u3001\u5b89\u88c5\u5e93\u201d \u4e09\u4e2a\u6838\u5fc3\u64cd\u4f5c&#xff0c;\u80fd\u72ec\u7acb\u914d\u7f6e AI \u9879\u76ee\u7684\u8fd0\u884c\u73af\u5883\u5373\u53ef&#xff0c;\u65e0\u9700\u6df1\u5165 conda \u7684\u9ad8\u7ea7\u529f\u80fd\u3002<\/li>\n<\/ul>\n<h4>3.2 Jupyter Notebook&#xff1a;AI \u4ea4\u4e92\u5f0f\u5f00\u53d1\u7684 \u201c\u795e\u5668\u201d<\/h4>\n<p>Jupyter Notebook \u662f AI \u9886\u57df\u6700\u6d41\u884c\u7684\u5f00\u53d1\u5de5\u5177&#xff0c;\u652f\u6301 \u201c\u4ee3\u7801\u5757 &#043; \u6587\u672c\u6ce8\u91ca &#043; \u56fe\u8868\u5c55\u793a\u201d \u7684\u4ea4\u4e92\u5f0f\u5f00\u53d1\u6a21\u5f0f&#xff0c;\u975e\u5e38\u9002\u5408 AI \u5b66\u4e60\u548c\u5b9e\u9a8c\u3002<\/p>\n<ul>\n<li>\u6838\u5fc3\u4f18\u52bf&#xff1a;\n<li>\u4ee3\u7801\u5206\u6bb5\u6267\u884c&#xff1a;\u65e0\u9700\u4e00\u6b21\u6027\u8fd0\u884c\u6240\u6709\u4ee3\u7801&#xff0c;\u53ef\u9010\u5757\u8c03\u8bd5&#xff08;\u5982\u5148\u8fd0\u884c\u6570\u636e\u8bfb\u53d6&#xff0c;\u518d\u8fd0\u884c\u6570\u636e\u6e05\u6d17&#xff09;&#xff0c;\u4fbf\u4e8e\u6392\u67e5\u9519\u8bef\u3002<\/li>\n<li>\u5b9e\u65f6\u5c55\u793a\u7ed3\u679c&#xff1a;\u4ee3\u7801\u8fd0\u884c\u7ed3\u679c&#xff08;\u5982\u6570\u636e\u8868\u683c\u3001\u56fe\u8868&#xff09;\u76f4\u63a5\u663e\u793a\u5728\u4ee3\u7801\u5757\u4e0b\u65b9&#xff0c;\u76f4\u89c2\u67e5\u770b\u3002<\/li>\n<li>\u652f\u6301 Markdown \u6ce8\u91ca&#xff1a;\u53ef\u6dfb\u52a0\u6587\u672c\u8bf4\u660e\u3001\u516c\u5f0f\u3001\u6d41\u7a0b\u56fe&#xff0c;\u8ba9\u4ee3\u7801\u66f4\u6613\u7406\u89e3&#xff08;\u5982\u7528 Markdown \u5199\u5b9e\u9a8c\u6b65\u9aa4\u3001\u516c\u5f0f\u63a8\u5bfc&#xff09;\u3002<\/li>\n<\/li>\n<li>AI \u4e2d\u9ad8\u9891\u64cd\u4f5c&#xff1a;\n<li>\u542f\u52a8\u65b9\u5f0f&#xff1a;\u6fc0\u6d3b conda \u73af\u5883\u540e&#xff0c;\u547d\u4ee4\u884c\u8f93\u5165jupyter notebook&#xff0c;\u81ea\u52a8\u5728\u6d4f\u89c8\u5668\u6253\u5f00\u754c\u9762\u3002<\/li>\n<li>\u6838\u5fc3\u64cd\u4f5c&#xff1a;\u65b0\u5efa Notebook&#xff08;\u9009\u62e9 Python \u73af\u5883&#xff09;\u3001\u8fd0\u884c\u4ee3\u7801\u5757&#xff08;Shift&#043;Enter&#xff09;\u3001\u6dfb\u52a0 \/ \u5220\u9664\u4ee3\u7801\u5757\u3001\u4fdd\u5b58\u6587\u4ef6&#xff08;.ipynb\u683c\u5f0f&#xff09;\u3002<\/li>\n<li>\u5b9e\u7528\u6280\u5de7&#xff1a;\n<ul>\n<li>\u9b54\u6cd5\u547d\u4ee4&#xff1a;%matplotlib inline&#xff08;\u8ba9\u56fe\u8868\u76f4\u63a5\u663e\u793a\u5728 Notebook \u4e2d&#xff09;\u3001%timeit&#xff08;\u6d4b\u8bd5\u4ee3\u7801\u8fd0\u884c\u65f6\u95f4&#xff09;\u3002<\/li>\n<li>\u4ee3\u7801\u8865\u5168&#xff1a;\u6309Tab\u952e\u81ea\u52a8\u8865\u5168\u53d8\u91cf\u540d\u3001\u51fd\u6570\u540d&#xff0c;\u63d0\u5347\u7f16\u7801\u6548\u7387\u3002<\/li>\n<li>\u9519\u8bef\u8c03\u8bd5&#xff1a;\u4ee3\u7801\u8fd0\u884c\u51fa\u9519\u65f6&#xff0c;Notebook \u4f1a\u663e\u793a\u8be6\u7ec6\u7684\u9519\u8bef\u4fe1\u606f&#xff0c;\u4fbf\u4e8e\u5b9a\u4f4d\u95ee\u9898\u3002<\/li>\n<\/ul>\n<\/li>\n<\/li>\n<li>\u5b66\u4e60\u91cd\u70b9&#xff1a;\u638c\u63e1 \u201c\u542f\u52a8\u3001\u65b0\u5efa\u3001\u8fd0\u884c\u4ee3\u7801\u5757\u3001\u4fdd\u5b58\u201d \u7684\u57fa\u672c\u64cd\u4f5c&#xff0c;\u719f\u6089\u9b54\u6cd5\u547d\u4ee4\u548c\u4ee3\u7801\u8865\u5168\u6280\u5de7&#xff0c;\u80fd\u5728 Notebook \u4e2d\u5b8c\u6210\u6570\u636e\u5904\u7406\u3001\u6a21\u578b\u8bad\u7ec3\u548c\u7ed3\u679c\u5c55\u793a\u5373\u53ef\u3002<\/li>\n<\/ul>\n<h4>3.3 Git&#xff1a;AI \u9879\u76ee\u7684 \u201c\u7248\u672c\u63a7\u5236\u5de5\u5177\u201d<\/h4>\n<p>Git \u662f\u5206\u5e03\u5f0f\u7248\u672c\u63a7\u5236\u5de5\u5177&#xff0c;\u7528\u4e8e\u7ba1\u7406\u4ee3\u7801\u7684\u4fee\u6539\u8bb0\u5f55&#xff0c;AI \u9879\u76ee\u4e2d\u4e0d\u53ef\u6216\u7f3a \u2014\u2014 \u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u4f1a\u4e0d\u65ad\u8c03\u6574\u4ee3\u7801&#xff08;\u5982\u4fee\u6539\u6570\u636e\u9884\u5904\u7406\u903b\u8f91\u3001\u8c03\u6574\u6a21\u578b\u53c2\u6570&#xff09;&#xff0c;Git \u80fd\u8ddf\u8e2a\u6bcf\u4e00\u6b21\u4fee\u6539&#xff0c;\u65b9\u4fbf\u56de\u6eda\u5230\u4e4b\u524d\u7684\u7248\u672c&#xff0c;\u540c\u65f6\u652f\u6301\u591a\u4eba\u534f\u4f5c\u5f00\u53d1\u3002<\/p>\n<ul>\n<li>\u6838\u5fc3\u6982\u5ff5&#xff1a;\n<li>\u4ed3\u5e93&#xff08;Repository&#xff09;&#xff1a;\u5b58\u50a8\u4ee3\u7801\u7684\u76ee\u5f55&#xff0c;\u5206\u4e3a\u672c\u5730\u4ed3\u5e93&#xff08;\u7535\u8111\u672c\u5730&#xff09;\u548c\u8fdc\u7a0b\u4ed3\u5e93&#xff08;\u5982 GitHub\u3001Gitee&#xff09;\u3002<\/li>\n<li>\u63d0\u4ea4&#xff08;Commit&#xff09;&#xff1a;\u4fdd\u5b58\u4ee3\u7801\u7684\u4fee\u6539\u8bb0\u5f55&#xff0c;\u6bcf\u6b21\u4fee\u6539\u540e\u63d0\u4ea4&#xff0c;\u5f62\u6210\u7248\u672c\u5386\u53f2\u3002<\/li>\n<li>\u5206\u652f&#xff08;Branch&#xff09;&#xff1a;\u521b\u5efa\u72ec\u7acb\u7684\u5f00\u53d1\u5206\u652f&#xff08;\u5982feature\/data_process&#xff09;&#xff0c;\u907f\u514d\u4fee\u6539\u4e3b\u5206\u652f\u4ee3\u7801&#xff0c;\u5f00\u53d1\u5b8c\u6210\u540e\u5408\u5e76\u5230\u4e3b\u5206\u652f\u3002<\/li>\n<\/li>\n<li>AI \u4e2d\u9ad8\u9891\u64cd\u4f5c&#xff1a;\n<li>\u57fa\u7840\u6d41\u7a0b&#xff08;\u672c\u5730\u4ed3\u5e93&#xff09;&#xff1a;\n<ul>\n<li>\u521d\u59cb\u5316\u4ed3\u5e93&#xff1a;git init&#xff08;\u5728\u9879\u76ee\u76ee\u5f55\u4e0b\u6267\u884c&#xff0c;\u521b\u5efa\u672c\u5730\u4ed3\u5e93&#xff09;\u3002<\/li>\n<li>\u6dfb\u52a0\u6587\u4ef6&#xff1a;git add \u6587\u4ef6\u540d&#xff08;\u5982git add data_process.ipynb&#xff09;\u6216git add .&#xff08;\u6dfb\u52a0\u6240\u6709\u4fee\u6539\u6587\u4ef6&#xff09;\u3002<\/li>\n<li>\u63d0\u4ea4\u4fee\u6539&#xff1a;git commit -m &#034;\u6ce8\u91ca\u4fe1\u606f&#034;&#xff08;\u5982git commit -m &#034;\u5b8c\u6210\u6570\u636e\u9884\u5904\u7406\u4ee3\u7801&#034;&#xff09;&#xff0c;\u8bb0\u5f55\u4fee\u6539\u5185\u5bb9\u3002<\/li>\n<li>\u67e5\u770b\u72b6\u6001&#xff1a;git status&#xff08;\u67e5\u770b\u5f53\u524d\u6587\u4ef6\u7684\u4fee\u6539\u72b6\u6001&#xff09;\u3001git log&#xff08;\u67e5\u770b\u63d0\u4ea4\u5386\u53f2&#xff09;\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\u8fdc\u7a0b\u4ed3\u5e93&#xff08;GitHub\/Gitee&#xff09;&#xff1a;\n<ul>\n<li>\u5173\u8054\u8fdc\u7a0b\u4ed3\u5e93&#xff1a;git remote add origin \u8fdc\u7a0b\u4ed3\u5e93\u5730\u5740&#xff08;\u5982 GitHub \u4ed3\u5e93 URL&#xff09;\u3002<\/li>\n<li>\u63a8\u9001\u4ee3\u7801&#xff1a;git push -u origin \u5206\u652f\u540d&#xff08;\u5c06\u672c\u5730\u4ee3\u7801\u63a8\u9001\u5230\u8fdc\u7a0b\u4ed3\u5e93&#xff0c;\u5907\u4efd\u548c\u5171\u4eab&#xff09;\u3002<\/li>\n<li>\u62c9\u53d6\u4ee3\u7801&#xff1a;git pull origin \u5206\u652f\u540d&#xff08;\u4ece\u8fdc\u7a0b\u4ed3\u5e93\u62c9\u53d6\u6700\u65b0\u4ee3\u7801&#xff0c;\u591a\u4eba\u534f\u4f5c\u65f6\u4f7f\u7528&#xff09;\u3002<\/li>\n<\/ul>\n<\/li>\n<\/li>\n<li>\u5b66\u4e60\u91cd\u70b9&#xff1a;\u638c\u63e1 \u201c\u521d\u59cb\u5316\u4ed3\u5e93\u3001\u6dfb\u52a0\u6587\u4ef6\u3001\u63d0\u4ea4\u4fee\u6539\u3001\u63a8\u9001 \/ \u62c9\u53d6\u8fdc\u7a0b\u4ed3\u5e93\u201d \u7684\u57fa\u7840\u6d41\u7a0b&#xff0c;\u80fd\u7ba1\u7406\u81ea\u5df1\u7684 AI \u9879\u76ee\u4ee3\u7801&#xff0c;\u56de\u6eda\u5230\u4e4b\u524d\u7684\u7248\u672c\u5373\u53ef&#xff0c;\u65e0\u9700\u6df1\u5165\u590d\u6742\u7684\u5206\u652f\u7ba1\u7406\u548c\u51b2\u7a81\u89e3\u51b3&#xff08;\u540e\u7eed\u5b9e\u6218\u4e2d\u518d\u9010\u6b65\u5b66\u4e60&#xff09;\u3002<\/li>\n<\/ul>\n<h3>\u56db\u3001\u5b9e\u6218\u8def\u7ebf\u4e0e\u5b66\u4e60\u5efa\u8bae<\/h3>\n<p>\u96f6\u57fa\u7840\u5b66\u4e60 AI \u7f16\u7a0b\u8bed\u8a00\u4e0e\u5de5\u5177&#xff0c;\u6700\u5bb9\u6613\u9677\u5165 \u201c\u53ea\u5b66\u4e0d\u7528\u201d \u7684\u8bef\u533a \u2014\u2014 \u8bed\u6cd5\u5b66\u4e86\u4e00\u5806&#xff0c;\u5374\u4e0d\u4f1a\u5904\u7406\u5b9e\u9645\u6570\u636e\u3001\u4e0d\u4f1a\u7528\u5de5\u5177\u5e93\u3002\u7ed3\u5408 AI \u5b9e\u6218\u573a\u666f&#xff0c;\u7ed9\u51fa\u4ee5\u4e0b\u9ad8\u6548\u5b66\u4e60\u5efa\u8bae&#xff1a;<\/p>\n<h4>4.1 \u6838\u5fc3\u539f\u5219&#xff1a;\u201c\u8fb9\u7ec3\u8fb9\u5b66&#xff0c;\u4ee5\u9879\u76ee\u9a71\u52a8\u201d<\/h4>\n<p>\u5de5\u5177\u7684\u4ef7\u503c\u5728\u4e8e \u201c\u4f7f\u7528\u201d&#xff0c;\u5efa\u8bae\u4ece\u7b80\u5355\u7684 AI \u5c0f\u9879\u76ee\u5165\u624b&#xff08;\u5982 \u201c\u9e22\u5c3e\u82b1\u5206\u7c7b\u201d\u201c\u623f\u4ef7\u9884\u6d4b\u201d&#xff09;&#xff0c;\u5728\u9879\u76ee\u4e2d\u5b66\u4e60&#xff1a;<\/p>\n<ul>\n<li>\u7528 Pandas \u8bfb\u53d6\u6570\u636e\u96c6&#xff0c;\u7528 Matplotlib\/Seaborn \u505a EDA \u5206\u6790&#xff1b;<\/li>\n<li>\u7528 NumPy \u505a\u7279\u5f81\u6807\u51c6\u5316\u3001\u77e9\u9635\u8fd0\u7b97&#xff1b;<\/li>\n<li>\u7528 Jupyter Notebook \u7f16\u5199\u4ee3\u7801\u3001\u5c55\u793a\u7ed3\u679c&#xff1b;<\/li>\n<li>\u7528 Git \u7ba1\u7406\u9879\u76ee\u4ee3\u7801\u3002\u901a\u8fc7\u9879\u76ee\u5c06\u8bed\u6cd5\u3001\u5e93\u3001\u5de5\u5177\u4e32\u8054\u8d77\u6765&#xff0c;\u907f\u514d \u201c\u788e\u7247\u5316\u5b66\u4e60\u201d\u3002<\/li>\n<\/ul>\n<h4>4.2 \u5b66\u4e60\u987a\u5e8f&#xff1a;\u4ece \u201c\u57fa\u7840\u201d \u5230 \u201c\u5b9e\u6218\u201d&#xff0c;\u5faa\u5e8f\u6e10\u8fdb<\/h4>\n<li>\u5148\u5b66Python \u6838\u5fc3\u8bed\u6cd5&#xff1a;\u805a\u7126\u6570\u636e\u7c7b\u578b\u3001\u51fd\u6570\u3001\u7c7b\u7684\u57fa\u7840\u7528\u6cd5&#xff0c;\u638c\u63e1 \u201c\u80fd\u5199\u7b80\u5355\u4ee3\u7801\u201d \u7684\u80fd\u529b&#xff1b;<\/li>\n<li>\u518d\u5b66\u4e09\u5927\u6570\u636e\u5904\u7406\u5e93&#xff1a;\u5148\u5b66 NumPy&#xff08;\u6570\u503c\u8ba1\u7b97\u57fa\u7840&#xff09;&#xff0c;\u518d\u5b66 Pandas&#xff08;\u6570\u636e\u5904\u7406\u6838\u5fc3&#xff09;&#xff0c;\u6700\u540e\u5b66 Matplotlib\/Seaborn&#xff08;\u53ef\u89c6\u5316&#xff09;&#xff0c;\u4e09\u8005\u914d\u5408\u7ec3\u4e60&#xff1b;<\/li>\n<li>\u6700\u540e\u5b66\u5f00\u53d1\u5de5\u5177&#xff1a;\u7528 Anaconda \u914d\u7f6e\u73af\u5883&#xff0c;\u7528 Jupyter Notebook \u505a\u5f00\u53d1&#xff0c;\u7528 Git \u7ba1\u7406\u4ee3\u7801&#xff0c;\u5f62\u6210\u5b8c\u6574\u7684\u5f00\u53d1\u6d41\u7a0b\u3002<\/li>\n<h4>4.3 \u907f\u5751\u6307\u5357&#xff1a;\u653e\u5f03 \u201c\u5168\u91cf\u5b66\u4e60\u201d&#xff0c;\u805a\u7126 \u201c\u5b9e\u6218\u9700\u6c42\u201d<\/h4>\n<ul>\n<li>\u4e0d\u8981\u6b7b\u8bb0\u786c\u80cc\u8bed\u6cd5&#xff1a;\u9047\u5230\u4e0d\u4f1a\u7684\u8bed\u6cd5&#xff0c;\u67e5\u5b98\u65b9\u6587\u6863&#xff08;\u5982 Python \u5b98\u65b9\u6587\u6863\u3001Pandas \u6587\u6863&#xff09;\u6216\u641c\u7d22\u5f15\u64ce&#xff0c;\u8fb9\u7528\u8fb9\u8bb0&#xff1b;<\/li>\n<li>\u4e0d\u8981\u8ffd\u6c42 \u201c\u5e93\u7684\u6240\u6709\u529f\u80fd\u201d&#xff1a;\u6bd4\u5982 Pandas \u6709\u4e0a\u767e\u4e2a\u51fd\u6570&#xff0c;\u53ea\u9700\u638c\u63e1 AI \u6570\u636e\u5904\u7406\u7684\u9ad8\u9891\u51fd\u6570&#xff08;\u5982read_csv\u3001fillna\u3001get_dummies&#xff09;&#xff1b;<\/li>\n<li>\u4e0d\u8981\u5bb3\u6015\u62a5\u9519&#xff1a;AI \u5f00\u53d1\u4e2d\u62a5\u9519\u662f\u5e38\u6001&#xff08;\u5982\u6570\u636e\u683c\u5f0f\u9519\u8bef\u3001\u5e93\u7248\u672c\u51b2\u7a81&#xff09;&#xff0c;\u5b66\u4f1a\u770b\u9519\u8bef\u4fe1\u606f&#xff0c;\u9010\u6b65\u6392\u67e5\u95ee\u9898&#xff08;\u5982 \u201cValueError: \u8f93\u5165\u6570\u636e\u7ef4\u5ea6\u4e0d\u5339\u914d\u201d&#xff0c;\u5148\u68c0\u67e5\u6570\u636e shape&#xff09;\u3002<\/li>\n<\/ul>\n<h4>4.4 \u8d44\u6e90\u63a8\u8350&#xff1a;\u9ad8\u6548\u83b7\u53d6\u5b66\u4e60\u8d44\u6599<\/h4>\n<ul>\n<li>\u5b98\u65b9\u6587\u6863&#xff1a;Python \u5b98\u65b9\u6587\u6863&#xff08;https:\/\/docs.python.org\/&#xff09;\u3001NumPy \u6587\u6863&#xff08;https:\/\/numpy.org\/doc\/&#xff09;\u3001Pandas \u6587\u6863&#xff08;https:\/\/pandas.pydata.org\/docs\/&#xff09;&#xff0c;\u6700\u6743\u5a01\u7684\u5b66\u4e60\u8d44\u6599&#xff1b;<\/li>\n<li>\u5b9e\u6218\u6559\u7a0b&#xff1a;Kaggle&#xff08;https:\/\/www.kaggle.com\/&#xff09;\u4e0a\u7684\u5165\u95e8\u9879\u76ee&#xff08;\u5982 \u201cTitanic \u751f\u5b58\u9884\u6d4b\u201d&#xff09;&#xff0c;\u5305\u542b\u5b8c\u6574\u7684\u6570\u636e\u96c6\u548c\u4ee3\u7801\u793a\u4f8b&#xff1b;<\/li>\n<li>\u5de5\u5177\u5b66\u4e60&#xff1a;Anaconda\u3001Jupyter Notebook\u3001Git 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