機器學(xué)習(xí)實戰(zhàn)營：從理論到實戰(zhàn)的探索之旅

定　價：￥68.00

作　者：	謝雪葵
出版社：	電子工業(yè)出版社
叢編項：
標　簽：	暫缺

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ISBN：	9787121478154	出版時間：	2024-05-01	包裝：	平裝-膠訂
開本：	128開	頁數(shù)：		字數(shù)：

內(nèi)容簡介

　　本書是一本機器學(xué)習(xí)實用指南，提供從基礎(chǔ)知識到進階技能的全面學(xué)習(xí)路徑。本書以淺顯易懂的方式介紹了機器學(xué)習(xí)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(xué)習(xí)的實操。此外，介紹了數(shù)據(jù)預(yù)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(xué)習(xí)的理解。本書適合對機器學(xué)習(xí)感興趣的初學(xué)者，也可作為軟件開發(fā)人員、數(shù)據(jù)分析師、學(xué)術(shù)研究人員的參考書籍。

作者簡介

　　謝雪葵，畢業(yè)于北郵軟件學(xué)院計算機科學(xué)系軟件工程專業(yè)。在校期間，多次獲得專業(yè)一、二等獎學(xué)金，并成功帶領(lǐng)團隊進行了校園APP的研發(fā)工作。阿誠網(wǎng)絡(luò)的創(chuàng)始人，該公司專注于為企業(yè)提供大數(shù)據(jù)相關(guān)服務(wù)。主要業(yè)務(wù)包括為企業(yè)提供大數(shù)據(jù)技術(shù)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(xué)習(xí)的預(yù)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(zé)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(xué)習(xí)技術(shù)進行風(fēng)險評估、用戶行為分析和產(chǎn)品推薦等。

圖書目錄

目錄
機器學(xué)習(xí)入門············································································1
機器學(xué)習(xí)簡介 ···········································································1
1.1.1 什么是機器學(xué)習(xí)································································1
1.1.2 機器學(xué)習(xí)的前景································································2
機器學(xué)習(xí)的主要類型 ··································································3
1.2.1 監(jiān)督學(xué)習(xí)·········································································4
1.2.2 無監(jiān)督學(xué)習(xí)······································································5
1.2.3 半監(jiān)督學(xué)習(xí)······································································7
1.2.4 強化學(xué)習(xí)·········································································8
1.2.5 監(jiān)督學(xué)習(xí)案例································································.10
選擇正確的算法·····································································.12
機器學(xué)習(xí)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(chǔ)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(luò)爬蟲) ····································.35
數(shù)據(jù)科學(xué)庫···········································································.38
2.2.1 NumPy ········································································.38
2.2.2 Pandas ·········································································.45
2.2.3 數(shù)據(jù)科學(xué)庫案例(電商網(wǎng)站) ··········································.54
機器學(xué)習(xí)庫···········································································.55
2.3.1 Scikit-Learn···································································.55
2.3.2 TensorFlow ···································································.60
2.3.3 Keras···········································································.64
2.3.4 機器學(xué)習(xí)庫案例(預(yù)測糖尿病) ·······································.67
數(shù)據(jù)預(yù)處理·············································································70
數(shù)據(jù)導(dǎo)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(xùn)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·························································.80
機器學(xué)習(xí)模型的構(gòu)建與評估························································84
監(jiān)督學(xué)習(xí)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(xué)習(xí)實戰(zhàn)·····································································.91
4.2.1 K-means ·······································································.92
4.2.2 主成分分析···································································.93
深度學(xué)習(xí)實戰(zhàn)········································································.95
4.3.1 神經(jīng)網(wǎng)絡(luò)······································································.95
4.3.2 卷積神經(jīng)網(wǎng)絡(luò)··············