每週一書《Python機器學習實踐指南 附隨書程式碼》分享！

機器學習是近年來漸趨熱門的一個領域，同時Python 語言經過一段時間的發展也已逐漸成為主流的程式語言之一。Python機器學習實踐指南結合了機器學習和Python 語言兩個熱門的領域，通過利用兩種核心的機器學習演算法來將Python 語言在資料分析方面的優勢發揮到極致。

全書共有10 章。第1 章講解了Python 機器學習的生態系統，剩餘9 章介紹了眾多與機器學習相關的演算法，包括各類分類演算法、資料視覺化技術、推薦引擎等，主要包括機器學習在公寓、機票、IPO 市場、新聞源、內容推廣、股票市場、影像、聊天機器人和推薦引擎等方面的應用。

本書適合Python 程式設計師、資料分析人員、對演算法感興趣的讀者、機器學習領域的從業人員及科研人員閱讀。

目錄

第1 章Python 機器學習的生態系統······1

1.1 資料科學/機器學習的工作

流程 ··································2

1.1.1 獲取··························2

1.1.2 檢查和探索·················2

1.1.3 清理和準備·················3

1.1.4 建模··························3

1.1.5 評估··························3

1.1.6 部署··························3

1.2 Python 庫和功能···················3

1.2.1 獲取··························4

1.2.2 檢查··························4

1.2.3 準備························20

1.2.4 建模和評估···············26

1.2.5 部署························34

1.3 設定機器學習的環境···········34

1.4 小結·································34

第2 章構建應用程式，發現低價的

公寓·············································35

2.1 獲取公寓房源資料··············36

使用import.io 抓取房源

資料 ·································36

2.2 檢查和準備資料·················38

2.2.1 分析資料···················46

2.2.2 視覺化資料················50

2.3 對資料建模························51

2.3.1 預測·························54

2.3.2 擴充套件模型···················57

2.4 小結·································57

第3 章構建應用程式，發現低價的

機票··································58

3.1 獲取機票價格資料···············59

3.2 使用高階的網路爬蟲技術

檢索票價資料·····················60

3.3 解析DOM 以提取定價資料····62

通過聚類技術識別

異常的票價·························66

3.4 使用IFTTT 傳送實時提醒······75

3.5 整合在一起························78

3.6 小結·································82

第4 章使用邏輯迴歸預測IPO 市場·······83

4.1 IPO 市場····························84

4.1.1 什麼是IPO ················84

4.1.2 近期IPO 市場表現·······84

4.1.3 基本的IPO 策略··········93

4.2 特徵工程···························94

4.3 二元分類··························103

4.4 特徵的重要性···················108

4.5 小結································111

第5 章建立自定義的新聞源··············112

5.1 使用Pocket 應用程式，建立一個

監督訓練的集合················112

5.1.1 安裝Pocket 的Chrome

擴充套件程式·················113

5.1.2 使用Pocket API 來檢索

故事·······················114

5.2 使用embed.ly API 下載故事的

內容 ·······························119

5.3 自然語言處理基礎·············120

5.4 支援向量機·······················123

5.5 IFTTT 與文章源、Google 表單

和電子郵件的整合·············125

通過IFTTT 設定新聞源

和 Google 表單···················125

5.6 設定你的每日個性化

新聞簡報·························133

5.7 小結································137

第6 章預測你的內容是否會廣為

流傳································138

6.1 關於病毒性，研究告訴我們了

些什麼 ····························139

6.2 獲取分享的數量和內容·········140

6.3 探索傳播性的特徵·············149

6.3.1 探索影像資料···········149

6.3.2 探索標題·················152

6.3.3 探索故事的內容········156

6.4 構建內容評分的預測模型····157

6.5 小結································162

第7 章使用機器學習預測股票市場·······163

7.1 市場分析的型別················164

7.2 關於股票市場，研究告訴

我們些什麼······················165

7.3 如何開發一個交易策略·······166

7.3.1 延長我們的分析

週期·······················172

7.3.2 使用支援向量迴歸，

構建我們的模型········175

7.3.3 建模與動態時間扭曲····182

7.4 小結·······························186

第8 章建立影像相似度的引擎···········187

8.1 影像的機器學習················188

8.2 處理影像·························189

8.3 查詢相似的影像················191

8.4 瞭解深度學習···················195

8.5 構建影像相似度的引擎·······198

8.6 小結·······························206

第9 章打造聊天機器人····················207

9.1 圖靈測試·························207

9.2 聊天機器人的歷史················208

9.3 聊天機器人的設計·············212

9.4 打造一個聊天機器人··········217

9.5 小結·······························227

第10 章構建推薦引擎·····················228

10.1 協同過濾························229

10.1.1 基於使用者的過濾······230

10.1.2 基於專案的過濾······233

10.2 基於內容的過濾···············236

10.3 混合系統························237

10.4 構建推薦引擎··················238

10.5 小結······························251

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