登录
首页 » Python » 机器学习实战

机器学习实战

于 2021-02-21 发布
0 532
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

  • autoencoder_v1
    用于数据自编码,非线性压缩,降维的一种方法;与PCA不同,pca为线性降维方式(Data auto encoding, nonlinear compression)
    2018-08-02 20:07:50下载
    积分:1
  • 分位回归
    说明:  多种方法实现分位数回归,有完整原理解释,直接可用。(Multiple methods for quantile regression)
    2020-03-03 14:37:14下载
    积分:1
  • 用matlab 实现了kmeans算法
    用matlab 实现了kmeans算法还附有评价指标计算(Matlab to achieve kmeans algorithm also attached to the evaluation index calculation)
    2020-06-19 04:40:01下载
    积分:1
  • 值算法
    常用数值算法源码,包括二分法、复化辛卜生公式、改进欧拉法、高斯-赛德尔迭代法、拉格郎日插值多项式、列主元高斯消去法、龙贝格算法、龙格-库塔算法、幂法、牛顿迭代法、牛顿值多项式、四阶阿当姆斯预测-校正公式、雅可比迭代法、自适应梯形公式(变步长)、最小二乘法 个人由于需要编写,完全可用(Common numerical algorithm source code, including dichotomy, complex Simpson formula, improved Euler method, Gauss-Seidel iteration method, Lagrange interpolation polynomial, column principal element Gauss elimination method, Runberg algorithm, Runge-Kutta algorithm, power method, Newton iteration method, Newton value polynomial, fourth-order Adams prediction-correction formula, Jacobi iteration method, adaptive ladder formula (variable) Step Length and Least Square Method Individuals are fully available because they need to write)
    2019-05-03 18:36:35下载
    积分:1
  • apcluster.m
    ap算法完成ap聚类操作 需要输入参数为数据集 偏向参数 输出结果为聚类数目(The AP algorithm completes the AP clustering operation, the input parameter is the data set bias parameter, and the output result is the number of clusters)
    2017-11-19 23:56:45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
  • hyglrpf
    零件切割问题 回溯算法的实现, 给定一块宽度为W的矩形板,矩形板的高度不受限制,现需要从板上分别切割出n个高度为hi,宽度()
    2018-03-15 11:17:34下载
    积分:1
  • 带阻尼参的pagerank实现
    资源描述实现基本的pagerank算法,可实现传递阻尼参数和迭代次数。源文件从文件中读取
    2022-09-10 21:45:04下载
    积分:1
  • 小波包能量谱
    用于信号特征提取,提取信号的小波包能量谱(Extracting the wavelet packet energy spectrum of the signal)
    2020-06-27 01:40:01下载
    积分:1
  • IABC_KMC_test_on_Iris_wine_glass
    基于人工蚁群的的改进Kmeans,进行了改进,效果较好(Based on the improvement of artificial ant colony, Kmeans have been improved and the effect is better)
    2017-05-20 20:24:27下载
    积分:1
  • 696516资源总数
  • 106914会员总数
  • 0今日下载