[FTUForum.com] [UDEMY] Beginner to Advanced Guide on Machine Learning with R Tool [FTU]

磁力链接/BT种子名称

[FTUForum.com] [UDEMY] Beginner to Advanced Guide on Machine Learning with R Tool [FTU]

磁力链接/BT种子简介

种子哈希：08fa1cc0fce7c5b246c1a62023a81991e9d164e5
文件大小：338.59M
已经下载：470次
下载速度：极快
收录时间：2021-05-10
最近下载：2025-10-30

移花宫入口

移花宫.com邀月.com怜星.com花无缺.comyhgbt.icuyhgbt.top

磁力链接下载

magnet:?xt=urn:btih:08FA1CC0FCE7C5B246C1A62023A81991E9D164E5
推荐使用PIKPAK网盘下载资源，10TB超大空间，不限制资源，无限次数离线下载，视频在线观看

下载BT种子文件

磁力链接 迅雷下载 PIKPAK在线播放 世界之窗 小蓝俱乐部 含羞草 欲漫涩 逼哩逼哩 成人快手 51品茶 51动漫 91短视频 抖音Max TikTok成人版 PornHub 暗网Xvideo 草榴社区 哆哔涩漫 呦乐园 萝莉岛 搜同

最近搜索

文件列表

  • 7. Module-7 Regression/7. 7.7 Implementation of Forecasting.mp4 40.0 MB
  • 3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.mp4 35.7 MB
  • 7. Module-7 Regression/6. 7.6 Forecasting.mp4 20.8 MB
  • 1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.mp4 18.5 MB
  • 2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.mp4 16.5 MB
  • 3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.mp4 15.4 MB
  • 7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.mp4 12.9 MB
  • 4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.mp4 12.9 MB
  • 6. Module-6 Clustering/2. 6.2 K-Means Clustering.mp4 11.8 MB
  • 5. Module-5 Tree Based Models/4. 5.4 Boosting.mp4 11.3 MB
  • 7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.mp4 10.8 MB
  • 4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.mp4 9.3 MB
  • 5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.mp4 9.1 MB
  • 6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.mp4 8.5 MB
  • 5. Module-5 Tree Based Models/3. 5.3 Bagging.mp4 8.1 MB
  • 5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.mp4 7.8 MB
  • 6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.mp4 7.5 MB
  • 7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.mp4 6.9 MB
  • 3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.mp4 6.7 MB
  • 3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.mp4 6.4 MB
  • 1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.mp4 6.4 MB
  • 2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.mp4 5.6 MB
  • 4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.mp4 5.6 MB
  • 3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.mp4 5.3 MB
  • 4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.mp4 5.2 MB
  • 5. Module-5 Tree Based Models/1. 5.1 Decision Tree.mp4 5.1 MB
  • 7. Module-7 Regression/4. 7.4 Logistic Regression.mp4 4.9 MB
  • 7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.mp4 4.8 MB
  • 4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.mp4 4.5 MB
  • 5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.mp4 4.3 MB
  • 1. Module-1 Introduction to Course/3. 1.3 What you will Learn.mp4 3.9 MB
  • 1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.mp4 3.7 MB
  • 2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.mp4 3.6 MB
  • 3. Module-3 Classification/1. 3.1 Introduction to Classification.mp4 3.4 MB
  • 4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.mp4 3.3 MB
  • 6. Module-6 Clustering/1. 6.1 Introduction to Clustering.mp4 3.0 MB
  • 4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.mp4 2.8 MB
  • 3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.mp4 2.5 MB
  • 3. Module-3 Classification/5. 3.5 Implementation of Naive-Bayes Classifier.vtt 15.2 kB
  • 2. Module-2 Introduction to validation and its Methods/3.1 Programs.zip.zip 11.2 kB
  • 3. Module-3 Classification/3.1 Programs.zip.zip 11.2 kB
  • 3. Module-3 Classification/5.1 Programs.zip.zip 11.2 kB
  • 3. Module-3 Classification/7.1 Programs.zip.zip 11.2 kB
  • 4. Module-4 Black Box Method-Neural network and SVM/3.1 Programs.zip.zip 11.2 kB
  • 4. Module-4 Black Box Method-Neural network and SVM/5.1 Programs.zip.zip 11.2 kB
  • 4. Module-4 Black Box Method-Neural network and SVM/7.1 Programs.zip.zip 11.2 kB
  • 5. Module-5 Tree Based Models/2.1 Programs.zip.zip 11.2 kB
  • 5. Module-5 Tree Based Models/3.1 Programs.zip.zip 11.2 kB
  • 5. Module-5 Tree Based Models/4.1 Programs.zip.zip 11.2 kB
  • 5. Module-5 Tree Based Models/6.1 Programs.zip.zip 11.2 kB
  • 6. Module-6 Clustering/3.1 Programs.zip.zip 11.2 kB
  • 6. Module-6 Clustering/4.1 Programs.zip.zip 11.2 kB
  • 7. Module-7 Regression/2.1 Programs.zip.zip 11.2 kB
  • 7. Module-7 Regression/3.1 Programs.zip.zip 11.2 kB
  • 7. Module-7 Regression/5.1 Programs.zip.zip 11.2 kB
  • 7. Module-7 Regression/7.1 Programs.zip.zip 11.2 kB
  • 2. Module-2 Introduction to validation and its Methods/3. 2.3 Caret package.vtt 8.4 kB
  • 6. Module-6 Clustering/2. 6.2 K-Means Clustering.vtt 7.8 kB
  • 3. Module-3 Classification/3. 3.3 Implementation of KNN Algorithm.vtt 6.7 kB
  • 5. Module-5 Tree Based Models/4. 5.4 Boosting.vtt 6.1 kB
  • 7. Module-7 Regression/2. 7.2 Implementation of Linear Regression.vtt 6.0 kB
  • 7. Module-7 Regression/3. 7.3 Multiple Covariates Regression.vtt 5.3 kB
  • 4. Module-4 Black Box Method-Neural network and SVM/3. 4.3 Implement Neural Network in R.vtt 5.1 kB
  • 1. Module-1 Introduction to Course/4. 1.4 Techniques of Machine Learning.vtt 4.3 kB
  • 4. Module-4 Black Box Method-Neural network and SVM/7. 4.7 Implementation of SVM in R.vtt 3.9 kB
  • 5. Module-5 Tree Based Models/2. 5.2 Implementation of Decision Tree.vtt 3.8 kB
  • 3. Module-3 Classification/2. 3.2 KNN- K Nearest Neighbors.vtt 3.7 kB
  • 2. Module-2 Introduction to validation and its Methods/2. 2.2 Cross Validation Method.vtt 3.7 kB
  • 5. Module-5 Tree Based Models/3. 5.3 Bagging.vtt 3.7 kB
  • 6. Module-6 Clustering/4. 6.4 Hierarchical Clustering.vtt 3.5 kB
  • 5. Module-5 Tree Based Models/6. 5.6 Implementation of Random Forest.vtt 3.4 kB
  • 6. Module-6 Clustering/3. 6.3 Implementation of K-Means Clustering.vtt 3.4 kB
  • 7. Module-7 Regression/5. 7.5 Implementation of Logistic Regression.vtt 3.2 kB
  • 3. Module-3 Classification/4. 3.4 Naive-Bayes Classifier.vtt 3.1 kB
  • 3. Module-3 Classification/7. 3.7 Implementation of Linear Discriminant Analysis.vtt 3.0 kB
  • 7. Module-7 Regression/6. 7.6 Forecasting.vtt 3.0 kB
  • 4. Module-4 Black Box Method-Neural network and SVM/6. 4.6 Introduction to Support Vector Machine.vtt 2.9 kB
  • 7. Module-7 Regression/4. 7.4 Logistic Regression.vtt 2.7 kB
  • 7. Module-7 Regression/7. 7.7 Implementation of Forecasting.vtt 2.7 kB
  • 5. Module-5 Tree Based Models/1. 5.1 Decision Tree.vtt 2.7 kB
  • 7. Module-7 Regression/1. 7.1 Predicting with Linear Regression.vtt 2.6 kB
  • 1. Module-1 Introduction to Course/1. 1.1 Introduction to the Course.vtt 2.6 kB
  • 4. Module-4 Black Box Method-Neural network and SVM/2. 4.2 Conceptualizing of Neural Network.vtt 2.5 kB
  • 5. Module-5 Tree Based Models/5. 5.5 Introduction to Random Forest.vtt 2.4 kB
  • 2. Module-2 Introduction to validation and its Methods/1. 2.1 Introduction to Cross Validation.vtt 2.4 kB
  • 1. Module-1 Introduction to Course/3. 1.3 What you will Learn.vtt 1.9 kB
  • 3. Module-3 Classification/1. 3.1 Introduction to Classification.vtt 1.9 kB
  • 6. Module-6 Clustering/1. 6.1 Introduction to Clustering.vtt 1.8 kB
  • 4. Module-4 Black Box Method-Neural network and SVM/4. 4.4 Back Propagation.vtt 1.7 kB
  • 4. Module-4 Black Box Method-Neural network and SVM/1. 4.1 Introduction to Artificial Neural Network.vtt 1.7 kB
  • 4. Module-4 Black Box Method-Neural network and SVM/5. 4.5 Implementation of Back Propagation Network.vtt 1.6 kB
  • 3. Module-3 Classification/6. 3.6 Linear Discriminant Analysis.vtt 1.3 kB
  • 1. Module-1 Introduction to Course/2. 1.2 Pre-Requisite.vtt 776 Bytes
  • 0. Websites you may like/1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url 328 Bytes
  • 0. Websites you may like/5. (Discuss.FTUForum.com) FTU Discussion Forum.url 294 Bytes
  • 0. Websites you may like/2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url 286 Bytes
  • 0. Websites you may like/4. (FTUApps.com) Download Cracked Developers Applications For Free.url 239 Bytes
  • 0. Websites you may like/How you can help Team-FTU.txt 237 Bytes
  • 0. Websites you may like/3. (NulledPremium.com) Download Cracked Website Themes, Plugins, Scripts And Stock Images.url 163 Bytes

随机展示

相关说明

本站不存储任何资源内容，只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符)，并提供查询服务，是一个完全合法的搜索引擎系统。 网站不提供种子下载服务，用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责，请用户注意甄别！