登录
首页 » Python » 机器学习实战

机器学习实战

于 2021-02-21 发布
0 285
下载积分: 1 下载次数: 4

代码说明:

说明:  机器学习实战中文英文pdf+数据集+代码(Practice of machine learning)

文件列表:

Machine-Learning-in-Action-master, 0 , 2020-02-05
Machine-Learning-in-Action-master\Ch02-KNN, 0 , 2020-02-05
Machine-Learning-in-Action-master\Ch02-KNN\2.1.py, 2547 , 2020-02-05
Machine-Learning-in-Action-master\Ch02-KNN\2.2.1.py, 1955 , 2020-02-05
Machine-Learning-in-Action-master\Ch02-KNN\2.2.2.py, 6095 , 2020-02-05
Machine-Learning-in-Action-master\Ch02-KNN\2.2.3.py, 2875 , 2020-02-05
Machine-Learning-in-Action-master\Ch02-KNN\2.2.4.py, 5630 , 2020-02-05
Machine-Learning-in-Action-master\Ch02-KNN\2.2.5.py, 5337 , 2020-02-05
Machine-Learning-in-Action-master\Ch02-KNN\2.3.2.py, 3022 , 2020-02-05
Machine-Learning-in-Action-master\Ch03-DecisionTree, 0 , 2020-02-05
Machine-Learning-in-Action-master\Ch03-DecisionTree\3.2.1-1.py, 2296 , 2020-02-05
Machine-Learning-in-Action-master\Ch03-DecisionTree\3.2.1-2.py, 4861 , 2020-02-05
Machine-Learning-in-Action-master\Ch03-DecisionTree\3.2.2.py, 6980 , 2020-02-05
Machine-Learning-in-Action-master\Ch03-DecisionTree\3.3.py, 13069 , 2020-02-05
Machine-Learning-in-Action-master\Ch03-DecisionTree\3.4.py, 8011 , 2020-02-05
Machine-Learning-in-Action-master\Ch03-DecisionTree\3.5.1.py, 626 , 2020-02-05
Machine-Learning-in-Action-master\Ch03-DecisionTree\3.5.2.py, 518 , 2020-02-05
Machine-Learning-in-Action-master\Ch03-DecisionTree\3.6.2-1.py, 1365 , 2020-02-05
Machine-Learning-in-Action-master\Ch03-DecisionTree\3.6.2-2.py, 1786 , 2020-02-05
Machine-Learning-in-Action-master\Ch03-DecisionTree\3.6.2-3.py, 2320 , 2020-02-05
Machine-Learning-in-Action-master\Ch04-NaiveBayes, 0 , 2020-02-05
Machine-Learning-in-Action-master\Ch04-NaiveBayes\4.7.1.py, 2622 , 2020-02-05
Machine-Learning-in-Action-master\Ch04-NaiveBayes\4.7.2.py, 4272 , 2020-02-05
Machine-Learning-in-Action-master\Ch04-NaiveBayes\4.7.3.py, 4387 , 2020-02-05
Machine-Learning-in-Action-master\Ch04-NaiveBayes\4.8.1.py, 1801 , 2020-02-05
Machine-Learning-in-Action-master\Ch04-NaiveBayes\4.8.2.py, 9564 , 2020-02-05
Machine-Learning-in-Action-master\Ch04-NaiveBayes\4.9.1.py, 1558 , 2020-02-05
Machine-Learning-in-Action-master\Ch04-NaiveBayes\4.9.2-1.py, 3677 , 2020-02-05
Machine-Learning-in-Action-master\Ch04-NaiveBayes\4.9.2-2.py, 5510 , 2020-02-05
Machine-Learning-in-Action-master\Ch04-NaiveBayes\4.9.2-3.py, 7586 , 2020-02-05
Machine-Learning-in-Action-master\Ch04-NaiveBayes\4.9.2-4.py, 7299 , 2020-02-05
Machine-Learning-in-Action-master\Ch05-Logistic, 0 , 2020-02-05
Machine-Learning-in-Action-master\Ch05-Logistic\5.4.1.py, 2606 , 2020-02-05
Machine-Learning-in-Action-master\Ch05-Logistic\5.4.2.py, 2460 , 2020-02-05
Machine-Learning-in-Action-master\Ch05-Logistic\5.4.3.py, 4086 , 2020-02-05
Machine-Learning-in-Action-master\Ch05-Logistic\5.4.4.py, 4297 , 2020-02-05
Machine-Learning-in-Action-master\Ch05-Logistic\5.4.5.py, 6763 , 2020-02-05
Machine-Learning-in-Action-master\Ch05-Logistic\5.5.2-1.py, 3270 , 2020-02-05
Machine-Learning-in-Action-master\Ch05-Logistic\5.5.2-2.py, 3076 , 2020-02-05
Machine-Learning-in-Action-master\Ch05-Logistic\5.6.py, 1353 , 2020-02-05
Machine-Learning-in-Action-master\Ch06-SVM, 0 , 2020-02-05
Machine-Learning-in-Action-master\Ch06-SVM\6.3.py, 7623 , 2020-02-05
Machine-Learning-in-Action-master\Ch06-SVM\6.4.py, 11636 , 2020-02-05
Machine-Learning-in-Action-master\Ch06-SVM\6.5.1.py, 1591 , 2020-02-05
Machine-Learning-in-Action-master\Ch06-SVM\6.5.2.py, 13616 , 2020-02-05
Machine-Learning-in-Action-master\Ch06-SVM\6.6.py, 170 , 2020-02-05
Machine-Learning-in-Action-master\Ch06-SVM\6.7.py, 2705 , 2020-02-05
Machine-Learning-in-Action-master\Ch07-AdaBoost, 0 , 2020-02-05
Machine-Learning-in-Action-master\Ch07-AdaBoost\7.3.1.py, 1506 , 2020-02-05
Machine-Learning-in-Action-master\Ch07-AdaBoost\7.3.2.py, 3697 , 2020-02-05
Machine-Learning-in-Action-master\Ch07-AdaBoost\7.4.1.py, 5141 , 2020-02-05
Machine-Learning-in-Action-master\Ch07-AdaBoost\7.4.2.py, 6479 , 2020-02-05
Machine-Learning-in-Action-master\Ch07-AdaBoost\7.5.py, 6291 , 2020-02-05
Machine-Learning-in-Action-master\Ch07-AdaBoost\7.6.py, 1440 , 2020-02-05
Machine-Learning-in-Action-master\Ch07-AdaBoost\7.8.py, 7149 , 2020-02-05
Machine-Learning-in-Action-master\Ch08-Regression, 0 , 2020-02-05
Machine-Learning-in-Action-master\Ch08-Regression\8.2.1.py, 1513 , 2020-02-05
Machine-Learning-in-Action-master\Ch08-Regression\8.2.2.py, 2170 , 2020-02-05
Machine-Learning-in-Action-master\Ch08-Regression\8.2.3.py, 1589 , 2020-02-05
Machine-Learning-in-Action-master\Ch08-Regression\8.3.py, 4174 , 2020-02-05
Machine-Learning-in-Action-master\Ch08-Regression\8.4.py, 4611 , 2020-02-05
Machine-Learning-in-Action-master\Ch08-Regression\8.5.1.py, 3257 , 2020-02-05
Machine-Learning-in-Action-master\Ch08-Regression\8.5.3.py, 4046 , 2020-02-05
Machine-Learning-in-Action-master\Ch08-Regression\8.6.1.py, 3130 , 2020-02-05
Machine-Learning-in-Action-master\Ch08-Regression\8.6.2-1.py, 4908 , 2020-02-05
Machine-Learning-in-Action-master\Ch08-Regression\8.6.2-2.py, 8240 , 2020-02-05
Machine-Learning-in-Action-master\Ch08-Regression\8.6.2-3.py, 6034 , 2020-02-05
Machine-Learning-in-Action-master\Ch08-Regression\8.7.py, 3473 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees, 0 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.3.py, 802 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.4.1.py, 1205 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.4.2.py, 3493 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.4.3.py, 4324 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.4.4.py, 1429 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.4.5.py, 4323 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.5.1-1.py, 1436 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.5.1-2.py, 4291 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.5.2.py, 7136 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.6.1.py, 1435 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.6.2.py, 5049 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.7.1.py, 1450 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.7.2.py, 6215 , 2020-02-05
Machine-Learning-in-Action-master\Ch09-Regression Trees\9.8.py, 2908 , 2020-02-05
Machine-Learning-in-Action-master\Machine Learning in Action.pdf, 6896910 , 2020-02-05
Machine-Learning-in-Action-master\README.md, 3285 , 2020-02-05
Machine-Learning-in-Action-master\机器学习实战.pdf, 10671473 , 2020-02-05
Machine-Learning-in-Action-master\机器学习实战总目录.md, 2431 , 2020-02-05
Machine-Learning-in-Action-master\机器学习实战数据集.zip, 17370427 , 2020-02-05

下载说明:请别用迅雷下载,失败请重下,重下不扣分!

发表评论

0 个回复

  • OPTICS
    此为利用optics聚类方法剔除风电异常数据后,采用极限学习机验证的代码(optics data mining)
    2017-03-22 19:29:22下载
    积分:1
  • 0056764
    这是一本经典数值算法书,包含多种算法的理论,为编程者具有一定参考意义()
    2018-05-25 16:07:55下载
    积分:1
  • havz-bhlding
    BP网络VC代码 其实这就是成型的算法,估计好多人写过(BP network VC code is actually a molding algorithm, estimated that a lot of people have written)
    2018-09-06 15:00:59下载
    积分:1
  • degreeor
    复杂网络聚类系数的matlab编程代码,将复杂网络存储为矩阵,再对其matLab编程,可得到度分布图(The MATLAB programming code of clustering coefficient of complex network is used to store complex network as matrix, and then matLab programming is used to get the degree distribution graph.)
    2018-05-23 05:28:45下载
    积分:1
  • TurbulentWindGenerator
    三维风场模拟.利用Kaimal spectrum结合FFT进行风场模拟,生成风速时程得进行必要参数的定义。(3D Turbulent Wind Generation。 Generation of three-dimensional turbulent wind fields, by employing a Kaimal spectrum and IEC-based coherence function. )
    2017-02-28 11:35:25下载
    积分:1
  • Python预处理
    Python数据预处理示例,包括数据清洗、数据整合、数据变换等操作。(Python data preprocessing examples, including data cleaning, data integration, data transformation and other operations.)
    2020-09-17 14:07:54下载
    积分:1
  • MSA
    自动生成Excel表格,包括测量系统分析MSA GR&R--均值极差法 ,方差分析,均值极差(Automatic generation of Excel tables, including measurement system analysis MSA GR&R - mean extreme difference method, variance analysis, mean extreme difference)
    2019-06-20 21:24:10下载
    积分:1
  • 技术在公安犯罪行为分析中的应用研究
    数据挖掘在经侦项目中的应用,本文用到python中的社区划分算法(In the application of data mining in economic investigation projects, this paper uses community partition algorithm in Python.)
    2020-07-03 08:00:02下载
    积分:1
  • my_apriori
    很好用的关联规则挖掘经典算法,推荐使用。包括支持度、置信度、提升度,输出结果到excel文件(Good use of association rules mining classic algorithm, recommended)
    2018-11-14 15:51:16下载
    积分:1
  • 从零开始学Python网络爬虫源代码+教学PPT
    说明:  《从零开始学爬虫》的配套资料(PPT和源码)("Learning Reptiles from Zero" (PPT and Source))
    2019-03-18 22:06:06下载
    积分:1
  • 696518资源总数
  • 104349会员总数
  • 32今日下载