人工智能目錄

第一章Numpy前導(dǎo)介紹,,,,,

"2'41""

1.1、Anconda安裝","2'41""",,"""",√,

"17'47""

1.2、JupyterNoteBook","17'47""",,"""",√,

"29'13""

1.3、Numpy介紹+ndarry","29'13""",,"""",√,

"7'1""

1.4、ndarry的shape屬性巧算","7'1""",,"""",√,

"31'46""

1.5、ndarray的常見創(chuàng)建方式","31'46""",,"""",√,

"24'18""

1.6、NumPy中的數(shù)據(jù)類型","24'18""",,"""",√,

"5'51""

1.7、NumPy數(shù)據(jù)類型2","5'51""",,"""",√,

"32'55""

1.8、Numpy基本操作","32'55""",,"""",√,

"21'30""

1.9、索引和切片","21'30""",,"""",√,

"17'22""

1.10、索引和切片（2）","17'22""",,"""",√,

"4'16""

1.11、數(shù)組轉(zhuǎn)制與軸兌換","4'16""",,"""",√,

"22'21""

1.12、通用函數(shù)","22'21""",,"""",√,

"15'27""

1.13、np.where函數(shù)","15'27""",,"""",√,

"5'42""

1.14、np.unique函數(shù)","5'42""",,"""",√,

"30'2""

1.15、數(shù)組數(shù)據(jù)文件讀取","30'2""",,"""",√,

第二章Pandas前導(dǎo)課程,,,,,

"11'22""

2.1、Pandas介紹","11'22""",,"""",√,

"34'56""

2.2、Series","34'56""",,"""",√,

"4'50""

2.3、索引對(duì)象","4'50""",,"""",√,

"17'36""

2.4、DataFrame","17'36""",,"""",√,

"33'49""

2.5、Pandas常用操作（1）","33'49""",,"""",√,

"27'13""

2.6、Pandas常用操作（2）","27'13""",,"""",√,

"27'4""

2.7、缺失值處理","27'4""",,"""",√,

"37'5""

2.8、pandas制圖","37'5""",,"""",√,

"28'14""

2.9、Matplotlib（1）","28'14""",,"""",√,

"35'12""

2.10、Matplotlib（2）","35'12""",,"""",√,

"19'27""

2.11、Matplotlib中文輸出解決","19'27""",,"""",√,

第三章機(jī)器學(xué)習(xí)（一）,,,,,

"43'6""

3.1、01機(jī)器學(xué)習(xí)定義及理性認(rèn)識(shí)","43'6""",,"""",√,

"48'33""

3.2、02機(jī)器學(xué)習(xí)商業(yè)應(yīng)用場(chǎng)景、機(jī)器學(xué)習(xí)分類","48'33""",,"""",√,

"50'49""

3.3、03機(jī)器學(xué)習(xí)開發(fā)流程","50'49""",,"""",√,

"18'30""

3.4、04模型評(píng)估方法和部署","18'30""",,"""",√,

"24'32""

3.5、05線性回歸原理推倒過程","24'32""",,"""",√,

"14'42""

3.6、06線性回歸基礎(chǔ)認(rèn)識(shí)及原理講解","14'42""",,"""",√,

"33'52""

3.7、07線性回歸案例分析","33'52""",,"""",√,

第四章機(jī)器學(xué)習(xí)（二）,,,,,

"88'2""

4.1、01_線性回歸案例1、正則項(xiàng)、梯度下降","88'2""",,"""",√,

"19'2""

4.2、02_梯度下降方法及回歸案例分析","19'2""",,"""",√,

"40'13""

4.3、03_線性回歸、lasso、ridge、ElasitcNet以及案例分析","40'13""",,"""",√,

"14'57""

4.4、04_邏輯回歸原理","14'57""",,"""",√,

"39'0""

4.5、05_邏輯回歸及案例分析","39'0""",,"""",√,

"12'28""

4.6、06_softmax回歸及案例分析","12'28""",,"""",√,

"20'58""

4.7、07_綜合案例分析","20'58""",,"""",√,

第五章機(jī)器學(xué)習(xí)三-決策樹,,,,,

"33'32""

5.1、01決策樹、屬性分割、信息增益","33'32""",,"""",√,

"28'10""

5.2、02信息增益的計(jì)算、模型評(píng)估、ID3、C4.5、CART_","28'10""",,"""",√,

"55'47""

5.3、03決策樹案例分析1","55'47""",,"""",√,

"20'58""

5.4、04決策樹案例分析二、過擬合、剪枝分析","20'58""",,"""",√,

"23'0""

5.5、05bagging、隨機(jī)森林、隨機(jī)森林案例分析","23'0""",,"""",√,

"28'34""

5.6、06GBDT、Adaboost原理講解","28'34""",,"""",√,

"15'49""

5.7、07Adaboost案例分析、綜合案例分析","15'49""",,"""",√,

第六章機(jī)器學(xué)習(xí)四-SVM支持向量機(jī),

,,,,

"44'12""

6.1、svm講解","44'12""",,"""",√,

"34'43""

6.2、核函數(shù)","34'43""",,"""",√,

"10'40""

6.3、代碼講解(一)","10'40""",,"""",√,

"41'5""

6.4、代碼講解(二","41'5""",,"""",√,

"46'34""

6.5、代碼講解(三)","46'34""",,"""",√,

"29'37""

6.6、代碼講解(四)","29'37""",,"""",√,

第七章機(jī)器學(xué)習(xí)五-聚類分析+貝葉斯,,,,,

"19'49""

7.1、01-聚類的相似性度量（距離公式）","19'49""",,"""",√,

"38'31""

7.2、02-聚類思想、kmeans聚類、kmeans聚類應(yīng)用案例","38'31""",,"""",√,

"32'0""

7.3、03-二分kmeans、kmeans++、kmeansII、canopy、mini-batchkm","32'0""",,"""",√,

"19'40""

7.4、04-聚類算法的衡量指標(biāo)及案例實(shí)現(xiàn)","19'40""",,"""",√,

"23'41""

7.5、05-層次聚類及實(shí)現(xiàn)案例","23'41""",,"""",√,

"25'54""

7.6、06-密度聚類","25'54""",,"""",√,

"35'0""

7.7、07-密度聚類案例實(shí)現(xiàn)、譜聚類、譜聚類案例實(shí)現(xiàn)","35'0""",,"""",√,

"34'7""

7.8、08-不同聚類效果對(duì)比實(shí)現(xiàn)、文本案例、圖片案例","34'7""",,"""",√,

"30'46""

7.9、09-樸素貝葉斯原理、案例1、案例2","30'46""",,"""",√,

"28'32""

7.10、10-貝葉斯網(wǎng)絡(luò)","28'32""",,"""",√,

"28'32""

7.11、11-貝葉斯網(wǎng)絡(luò)拓展","28'32""",,"""",√,

第八章機(jī)器學(xué)習(xí)六-EM-HMM-LDA-ML,,,,,

"39'47""

8.1、01.EM算法講解","39'47""",,"""",√,

"52'58""

8.2、02.HMM及中文分詞","52'58""",,"""",√,

"47'11""

8.3、03.主題模型","4