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機器學習實戰(zhàn)營:從理論到實戰(zhàn)的探索之旅 讀者對象:本書適合對機器學習感興趣的初學者,也可作為軟件開發(fā)人員、數(shù)據(jù)分析師、學術研究人員的參考書籍。
本書是一本機器學習實用指南,提供從基礎知識到進階技能的全面學習路徑。本書以淺顯 易懂的方式介紹了機器學習的基本概念和主要類型,并詳細介紹使用 Python 及常見的庫進行數(shù) 據(jù)處理和機器學習的實操。此外,介紹了數(shù)據(jù)預處理的詳細過程,最后通過若干典型案例加深 讀者對機器學習的理解。本書適合對機器學習感興趣的初學者,也可作為軟件開發(fā)人員、數(shù)據(jù)分析師、學術研究人員的參考書籍。
謝雪葵,畢業(yè)于北郵軟件學院計算機科學系軟件工程專業(yè)。在校期間,多次獲得專業(yè)一、二等獎學金,并成功帶領團隊進行了校園APP的研發(fā)工作。阿誠網(wǎng)絡的創(chuàng)始人,該公司專注于為企業(yè)提供大數(shù)據(jù)相關服務。主要業(yè)務包括為企業(yè)提供大數(shù)據(jù)技術支持和降低成本、提高效率的解決方案,同時也提供基于機器學習的預測模型和智能決策支持。在過去的多年里,積累了豐富的企業(yè)級大數(shù)據(jù)項目實戰(zhàn)經(jīng)驗,并負責大型銀行和互聯(lián)網(wǎng)公司的大數(shù)據(jù)項目開發(fā)和性能優(yōu)化工作,其中包括使用機器學習技術進行風險評估、用戶行為分析和產(chǎn)品推薦等。
目錄
機器學習入門············································································1 機器學習簡介 ···········································································1 1.1.1 什么是機器學習································································1 1.1.2 機器學習的前景································································2 機器學習的主要類型 ··································································3 1.2.1 監(jiān)督學習·········································································4 1.2.2 無監(jiān)督學習······································································5 1.2.3 半監(jiān)督學習······································································7 1.2.4 強化學習·········································································8 1.2.5 監(jiān)督學習案例································································.10 選擇正確的算法·····································································.12 機器學習工具和環(huán)境·································································14 Python 介紹···········································································.14 2.1.1 Python 的安裝 ·······························································.14 2.1.2 Python 基礎語法 ····························································.19 2.1.3 Python 其他特性 ····························································.24 2.1.4 Python 簡單實戰(zhàn)案例(猜字游戲) ····································.31 2.1.5 Python 高級實戰(zhàn)案例(網(wǎng)絡爬蟲) ····································.35 數(shù)據(jù)科學庫···········································································.38 2.2.1 NumPy ········································································.38 2.2.2 Pandas ·········································································.45 2.2.3 數(shù)據(jù)科學庫案例(電商網(wǎng)站) ··········································.54 機器學習庫···········································································.55 2.3.1 Scikit-Learn···································································.55 2.3.2 TensorFlow ···································································.60 2.3.3 Keras···········································································.64 2.3.4 機器學習庫案例(預測糖尿病) ·······································.67 數(shù)據(jù)預處理·············································································70 數(shù)據(jù)導入 ··············································································.70 數(shù)據(jù)清洗 ··············································································.71 特征工程 ··············································································.73 3.3.1 特征選擇······································································.73 3.3.2 特征轉(zhuǎn)換······································································.75 3.3.3 特征縮放······································································.77 數(shù)據(jù)分割 ··············································································.78 3.4.1 訓練集·········································································.78 3.4.2 測試集·········································································.79 3.4.3 驗證集·········································································.80 案例分析:銀行客戶數(shù)據(jù)·························································.80 機器學習模型的構建與評估························································84 監(jiān)督學習實戰(zhàn)········································································.84 4.1.1 線性回歸······································································.84 4.1.2 邏輯回歸······································································.86 4.1.3 決策樹·········································································.88 4.1.4 隨機森林······································································.90 無監(jiān)督學習實戰(zhàn)·····································································.91 4.2.1 K-means ·······································································.92 4.2.2 主成分分析···································································.93 深度學習實戰(zhàn)········································································.95 4.3.1 神經(jīng)網(wǎng)絡······································································.95 4.3.2 卷積神經(jīng)網(wǎng)絡································································.98 4.3.3 循環(huán)神經(jīng)網(wǎng)絡································································102 模型評估與選擇 ·····································································105 案例分析:客戶流失預測 ·························································107 第5章 5.1 機器學習項目實戰(zhàn)···································································111 項目一:房價預測 ·································································.111 5.1.1 數(shù)據(jù)獲取與理解·····························································112 5.1.2 數(shù)據(jù)預處理···································································116 5.1.3 特征工程······································································120 5.1.4 模型構建與訓練·····························································123 5.1.5 模型評估與優(yōu)化·····························································125 5.1.6 結果解釋······································································128 項目二:圖像識別 ··································································130 5.2.1 數(shù)據(jù)獲取與理解·····························································131 5.2.2 數(shù)據(jù)預處理···································································134 5.2.3 特征工程······································································136 5.2.4 模型構建與訓練·····························································138 5.2.5 模型評估與優(yōu)化·····························································140 5.2.6 結果解釋······································································143 項目三:自然語言處理 ····························································144 5.3.1 數(shù)據(jù)獲取與理解·····························································144 5.3.2 數(shù)據(jù)預處理···································································147 5.3.3 特征工程······································································148 5.3.4 模型構建與訓練·····························································149 5.3.5 模型評估與優(yōu)化·····························································151 5.3.6 結果解釋······································································157 項目四:新聞主題分類 ····························································157 5.4.1 數(shù)據(jù)獲取與理解·····························································158 5.4.2 數(shù)據(jù)預處理···································································161 5.4.3 特征工程······································································164 5.4.4 模型構建與訓練·····························································166 5.4.5 模型評估與優(yōu)化·····························································168 5.4.6 結果解釋······································································171 項目五:信用卡欺詐檢測 ·························································172 5.5.1 數(shù)據(jù)獲取與理解·····························································173 5.5.2 數(shù)據(jù)預處理···································································176 第5章 5.1 機器學習項目實戰(zhàn)···································································111 項目一:房價預測 ·································································.111 5.1.1 數(shù)據(jù)獲取與理解·····························································112 5.1.2 數(shù)據(jù)預處理···································································116 5.1.3 特征工程······································································120 5.1.4 模型構建與訓練·····························································123 5.1.5 模型評估與優(yōu)化·····························································125 5.1.6 結果解釋······································································128 項目二:圖像識別 ··································································130 5.2.1 數(shù)據(jù)獲取與理解·····························································131 5.2.2 數(shù)據(jù)預處理···································································134 5.2.3 特征工程······································································136 5.2.4 模型構建與訓練·····························································138 5.2.5 模型評估與優(yōu)化·····························································140 5.2.6 結果解釋······································································143 項目三:自然語言處理 ····························································144 5.3.1 數(shù)據(jù)獲取與理解·····························································144 5.3.2 數(shù)據(jù)預處理···································································147 5.3.3 特征工程······································································148 5.3.4 模型構建與訓練·····························································149 5.3.5 模型評估與優(yōu)化·····························································151 5.3.6 結果解釋······································································157 項目四:新聞主題分類 ····························································157 5.4.1 數(shù)據(jù)獲取與理解·····························································158 5.4.2 數(shù)據(jù)預處理···································································161 5.4.3 特征工程······································································164 5.4.4 模型構建與訓練·····························································166 5.4.5 模型評估與優(yōu)化·····························································168 5.4.6 結果解釋······································································171 項目五:信用卡欺詐檢測 ·························································172 5.5.1 數(shù)據(jù)獲取與理解·····························································173 5.5.2 數(shù)據(jù)預處理···································································176
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