機器學習基礎與應用
皮衛,嚴麗麗,錢月鐘
- 出版商: 電子工業
- 出版日期: 2026-06-01
- 售價: $270
- 語言: 簡體中文
- 頁數: 164
- ISBN: 7121529890
- ISBN-13: 9787121529894
-
相關分類:
Machine Learning
下單後立即進貨 (約4週~6週)
相關主題
商品描述
本教材以機器學習的核心理念和技術手段為主線，堅守“立德樹人”的教育宗旨，以培養技術技能型人才、激發讀者學習興趣、增強讀者實踐能力為目標，采用信息化教學方式，將前沿的機器學習思想和方法融入課程之中，強化學生利用這些技術解決實際問題的能力，力圖塑造具有社會責任感、創新精神和團隊合作意識的新時代人工智能技術工作者。 教材中以多個真實的項目案例(交通車流量預測、二手車價格預測、共享單車租賃數量預測等)為導向，由淺入深、循序漸進地展開教學，內容涵蓋機器學習的基本概念、數據預處理的關鍵技術、分類、回歸、聚類等核心算法及其在互聯網、交通、金融、電商等多個領域的具體應用，以及如何通過集成方法提升模型性能的策略。全書分為三大篇，基礎知識篇，幫助讀者建立對機器學習基本概念的理解並掌握數據預處理的技巧；算法應用篇，深入探討核心算法在不同行業的應用實踐；綜合實踐篇，聚焦於集成學習方法的研究與應用，指導讀者有效整合多種算法，進一步提升模型的預測準確性和魯棒性。本教材旨在通過理論與實戰相結合的方式，培養能夠使用機器學習方法解決實際工作任務的技術技能型人才。 本教材適合作為高等職業學校人工智能、大數據等相關專業的教材，同時也適合作為需要充實人工智能、大數據知識的技術人員、愛好者的參考用書。
目錄大綱
第 1 篇 基礎知識篇
模塊 1 機器學習基礎知識···················································································2
1.1 人工智能、機器學習與深度學習的關系 ······················································2
1.2 機器學習的定義 ····················································································3
1.3 機器學習的主要任務 ··············································································3
1.4 機器學習的學習方式 ··············································································4
1.5 機器學習的三要素 ·················································································5
1.5.1 數據··························································································5
1.5.2 算法··························································································6
1.5.3 模型··························································································6
1.6 背景介紹 ·····························································································6
1.7 數據說明 ·····························································································6
1.8 任務實現 ·····························································································7
模塊小結 ·································································································.13
鞏固練習 ·································································································.13
模塊 2 數據預處理·························································································.14
2.1 數據的定義與類別 ··············································································.14
2.1.1 數據的定義··············································································.14
2.1.2 數據的類別··············································································.15
2.2 數據預處理技術 ·················································································.16
2.3 數據標準化技術 ·················································································.17
2.4 數據編碼技術 ····················································································.17
2.5 背景介紹 ··························································································.18
2.6 數據說明 ··························································································.18
2.7 任務實現 ··························································································.18
模塊小結 ·································································································.26
鞏固練習 ·································································································.26
第 2 篇 算法應用篇
模塊 3 基於線性回歸的交通車流量預測······························································.29
3.1 線性回歸的定義 ·················································································.29
3.2 回歸模型評估指標 ··············································································.30
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機器學習基礎與應用
3.3 背景介紹 ··························································································.31
3.4 數據說明 ··························································································.32
3.5 任務實現 ··························································································.32
模塊小結 ·································································································.37
鞏固練習 ·································································································.37
模塊 4 基於 k-NN 算法的二手車價格預測 ···························································.38
4.1 k-NN 算法的定義················································································.38
4.2 k-NN 算法實現流程·············································································.39
4.2.1 距離的計算方法········································································.40
4.2.2 K 值的選取··············································································.40
4.3 k-NN 算法的優缺點·············································································.41
4.4 背景介紹 ··························································································.42
4.5 數據說明 ··························································································.42
4.6 任務實現 ··························································································.43
模塊小結 ·································································································.50
鞏固練習 ·································································································.50
模塊 5 基於隨機森林算法的共享單車租賃數量預測···············································.52
5.1 集成學習的概念 ·················································································.52
5.2 隨機森林算法介紹 ··············································································.53
5.2.1 決策樹概念··············································································.53
5.2.2 Bagging 方法············································································.54
5.2.3 隨機森林算法實現流程·······························································.54
5.3 背景介紹 ··························································································.55
5.4 數據說明 ··························································································.56
5.5 任務實現 ··························································································.56
模塊小結 ·································································································.64
鞏固練習 ·································································································.65
模塊 6 基於 k 均值聚類算法的信用卡客戶劃分·····················································.66
6.1 聚類的概念 ·······················································································.66
6.2 k 均值聚類算法實現流程 ······································································.67
6.3 聚類數量選擇方法：手肘法 ··································································.68
6.4 聚類任務評估指標 ··············································································.68
6.5 背景介紹 ··························································································.69
6.6 數據說明 ··························································································.69
6.7 任務實現 ··························································································.70
模塊小結 ·································································································.76
鞏固練習 ·································································································.76
模塊 7 基於邏輯回歸的信用卡異常行為檢測························································.78
7.1 邏輯回歸算法定義 ··············································································.78
7.2 邏輯回歸算法原理 ··············································································.79
VI
目 錄
7.2.1 邏輯回歸二分類算法原理····························································.79
7.2.2 邏輯回歸多分類算法原理····························································.80
7.3 K 折交叉驗證法 ·················································································.80
7.4 背景介紹 ··························································································.81
7.5 數據說明 ··························································································.82
7.6 任務實現 ··························································································.82
模塊小結 ·································································································.88
鞏固練習 ·································································································.88
模塊 8 基於支持向量機的路面狀況分類······························································.90
8.1 支持向量機算法定義 ···········································································.90
8.2 支持向量機算法原理 ···········································································.91
8.2.1 超平面····················································································.91
8.2.2 超平面的選擇···········································································.93
8.2.3 核函數····················································································.94
8.3 圖像及其特征 ····················································································.94
8.3.1 數字圖像·················································································.94
8.3.2 灰度圖像·················································································.95
8.3.3 圖像特征提取方法·····································································.95
8.4 背景介紹 ··························································································.96
8.5 數據說明 ··························································································.97
8.6 任務實現 ··························································································.97
模塊小結 ·································································································106
鞏固練習 ·································································································106
模塊 9 基於樸素貝葉斯算法的店鋪評論分類························································107
9.1 樸素貝葉斯算法概述 ···········································································107
9.2 樸素貝葉斯算法原理 ···········································································108
9.2.1 貝葉斯定理··············································································108
9.2.2 樸素貝葉斯算法原理··································································108
9.3 文本分類數據預處理流程 ·····································································109
9.3.1 分詞······················································································.110
9.3.2 去除停用詞·············································································.111
9.3.3 文本特征提取··········································································.111
9.3.4 文本向量化·············································································.112
9.4 背景介紹 ·························································································.112
9.5 數據說明 ·························································································.112
9.6 任務實現 ·························································································.113
模塊小結 ································································································.118
鞏固練習 ································································································.118
模塊 10 基於多層感知機的相冊分類 ·································································.119
10.1 感知機算法······················································································120
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機器學習基礎與應用
10.2 多層感知機算法················································································120
10.3 圖像增廣·························································································121
10.3.1 圖像增廣的概念 ······································································121
10.3.2 圖像增廣的常用方法 ································································121
10.4 背景介紹·························································································123
10.5 數據說明·························································································123
10.6 任務實現·························································································124
模塊小結 ·································································································132
鞏固練習 ·································································································132
第 3 篇 綜合實踐篇
模塊 11 智慧電商：基於多模型融合實現商品銷量預測 ··········································134
11.1 Stacking 算法概述··············································································134
11.2 Stacking 算法流程··············································································135
11.3 GBDT 算法概述 ················································································136
11.4 GBDT 算法原理 ················································································136
11.5 背景介紹 ·························································································137
11.6 數據說明 ·························································································138
11.7 任務實現 ·························································································138
模塊小結 ·································································································143
鞏固練習 ·································································································143
模塊 12 智慧風控：基於多模型融合實現電信客戶流失預警····································145
12.1 XGBoost 算法概述·············································································145
12.2 XGBoost 算法原理·············································································146
12.3 XGBoost 算法的優缺點·······································································147
12.4 背景介紹·························································································148
12.5 數據說明·························································································148
12.6 任務實現·························································································148
模塊小結 ·································································································153
鞏固練習 ·································································································153
